{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Guided Project: Clean and Analyse Exit Surveys\n", "\n", "In this guided project, we worked with exit surveys from employees of the [Department of Education, Training and Employment](https://en.wikipedia.org/wiki/Department_of_Education_(Queensland)) (DETE) and the Technical and Further Education (TAFE) institute in Queensland, Australia. You can find the TAFE exit survey here and the survey for the DETE [here](https://data.gov.au/dataset/ds-qld-fe96ff30-d157-4a81-851d-215f2a0fe26d/details?q=exit%20survey).\n", "\n", "## Aim:\n", "- To analyse various reasons of resignations mainly dissatisfaction.\n", "- To analyse according to the age, experience and time worked at the company.\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns\n", "\n", "dete_survey = pd.read_csv('dete_survey.csv')\n", "tafe_survey = pd.read_csv('tafe_survey.csv')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDSeparationTypeCease DateDETE Start DateRole Start DatePositionClassificationRegionBusiness UnitEmployment Status...Kept informedWellness programsHealth & SafetyGenderAgeAboriginalTorres StraitSouth SeaDisabilityNESB
01Ill Health Retirement08/201219842004Public ServantA01-A04Central OfficeCorporate Strategy and PeformancePermanent Full-time...NNNMale56-60NaNNaNNaNNaNYes
12Voluntary Early Retirement (VER)08/2012Not StatedNot StatedPublic ServantAO5-AO7Central OfficeCorporate Strategy and PeformancePermanent Full-time...NNNMale56-60NaNNaNNaNNaNNaN
23Voluntary Early Retirement (VER)05/201220112011Schools OfficerNaNCentral OfficeEducation QueenslandPermanent Full-time...NNNMale61 or olderNaNNaNNaNNaNNaN
34Resignation-Other reasons05/201220052006TeacherPrimaryCentral QueenslandNaNPermanent Full-time...ANAFemale36-40NaNNaNNaNNaNNaN
45Age Retirement05/201219701989Head of Curriculum/Head of Special EducationNaNSouth EastNaNPermanent Full-time...NAMFemale61 or olderNaNNaNNaNNaNNaN
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5 rows × 56 columns

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" ], "text/plain": [ " ID SeparationType Cease Date DETE Start Date \\\n", "0 1 Ill Health Retirement 08/2012 1984 \n", "1 2 Voluntary Early Retirement (VER) 08/2012 Not Stated \n", "2 3 Voluntary Early Retirement (VER) 05/2012 2011 \n", "3 4 Resignation-Other reasons 05/2012 2005 \n", "4 5 Age Retirement 05/2012 1970 \n", "\n", " Role Start Date Position \\\n", "0 2004 Public Servant \n", "1 Not Stated Public Servant \n", "2 2011 Schools Officer \n", "3 2006 Teacher \n", "4 1989 Head of Curriculum/Head of Special Education \n", "\n", " Classification Region Business Unit \\\n", "0 A01-A04 Central Office Corporate Strategy and Peformance \n", "1 AO5-AO7 Central Office Corporate Strategy and Peformance \n", "2 NaN Central Office Education Queensland \n", "3 Primary Central Queensland NaN \n", "4 NaN South East NaN \n", "\n", " Employment Status ... Kept informed Wellness programs \\\n", "0 Permanent Full-time ... N N \n", "1 Permanent Full-time ... N N \n", "2 Permanent Full-time ... N N \n", "3 Permanent Full-time ... A N \n", "4 Permanent Full-time ... N A \n", "\n", " Health & Safety Gender Age Aboriginal Torres Strait South Sea \\\n", "0 N Male 56-60 NaN NaN NaN \n", "1 N Male 56-60 NaN NaN NaN \n", "2 N Male 61 or older NaN NaN NaN \n", "3 A Female 36-40 NaN NaN NaN \n", "4 M Female 61 or older NaN NaN NaN \n", "\n", " Disability NESB \n", "0 NaN Yes \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", "[5 rows x 56 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_survey.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 822 entries, 0 to 821\n", "Data columns (total 56 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 ID 822 non-null int64 \n", " 1 SeparationType 822 non-null object\n", " 2 Cease Date 822 non-null object\n", " 3 DETE Start Date 822 non-null object\n", " 4 Role Start Date 822 non-null object\n", " 5 Position 817 non-null object\n", " 6 Classification 455 non-null object\n", " 7 Region 822 non-null object\n", " 8 Business Unit 126 non-null object\n", " 9 Employment Status 817 non-null object\n", " 10 Career move to public sector 822 non-null bool \n", " 11 Career move to private sector 822 non-null bool \n", " 12 Interpersonal conflicts 822 non-null bool \n", " 13 Job dissatisfaction 822 non-null bool \n", " 14 Dissatisfaction with the department 822 non-null bool \n", " 15 Physical work environment 822 non-null bool \n", " 16 Lack of recognition 822 non-null bool \n", " 17 Lack of job security 822 non-null bool \n", " 18 Work location 822 non-null bool \n", " 19 Employment conditions 822 non-null bool \n", " 20 Maternity/family 822 non-null bool \n", " 21 Relocation 822 non-null bool \n", " 22 Study/Travel 822 non-null bool \n", " 23 Ill Health 822 non-null bool \n", " 24 Traumatic incident 822 non-null bool \n", " 25 Work life balance 822 non-null bool \n", " 26 Workload 822 non-null bool \n", " 27 None of the above 822 non-null bool \n", " 28 Professional Development 808 non-null object\n", " 29 Opportunities for promotion 735 non-null object\n", " 30 Staff morale 816 non-null object\n", " 31 Workplace issue 788 non-null object\n", " 32 Physical environment 817 non-null object\n", " 33 Worklife balance 815 non-null object\n", " 34 Stress and pressure support 810 non-null object\n", " 35 Performance of supervisor 813 non-null object\n", " 36 Peer support 812 non-null object\n", " 37 Initiative 813 non-null object\n", " 38 Skills 811 non-null object\n", " 39 Coach 767 non-null object\n", " 40 Career Aspirations 746 non-null object\n", " 41 Feedback 792 non-null object\n", " 42 Further PD 768 non-null object\n", " 43 Communication 814 non-null object\n", " 44 My say 812 non-null object\n", " 45 Information 816 non-null object\n", " 46 Kept informed 813 non-null object\n", " 47 Wellness programs 766 non-null object\n", " 48 Health & Safety 793 non-null object\n", " 49 Gender 798 non-null object\n", " 50 Age 811 non-null object\n", " 51 Aboriginal 16 non-null object\n", " 52 Torres Strait 3 non-null object\n", " 53 South Sea 7 non-null object\n", " 54 Disability 23 non-null object\n", " 55 NESB 32 non-null object\n", "dtypes: bool(18), int64(1), object(37)\n", "memory usage: 258.6+ KB\n" ] } ], "source": [ "dete_survey.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Most of the columns contain values of type object, including the fields corresponding to dates.\n", "- There were also observed some values of type boolean(might help us in classifying type of resignation).\n", "- In the last five columns, null values predominate almost entirely(most likely to be dropped)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ID 0\n", "SeparationType 0\n", "Cease Date 0\n", "DETE Start Date 0\n", "Role Start Date 0\n", "Position 5\n", "Classification 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"Wellness programs 56\n", "Health & Safety 29\n", "Gender 24\n", "Age 11\n", "Aboriginal 806\n", "Torres Strait 819\n", "South Sea 815\n", "Disability 799\n", "NESB 790\n", "dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_survey.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Record IDInstituteWorkAreaCESSATION YEARReason for ceasing employmentContributing Factors. Career Move - Public SectorContributing Factors. Career Move - Private SectorContributing Factors. Career Move - Self-employmentContributing Factors. Ill HealthContributing Factors. Maternity/Family...Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination?Workplace. Topic:Does your workplace promote and practice the principles of employment equity?Workplace. Topic:Does your workplace value the diversity of its employees?Workplace. Topic:Would you recommend the Institute as an employer to others?Gender. What is your Gender?CurrentAge. Current AgeEmployment Type. Employment TypeClassification. ClassificationLengthofServiceOverall. Overall Length of Service at Institute (in years)LengthofServiceCurrent. Length of Service at current workplace (in years)
06.341330e+17Southern Queensland Institute of TAFENon-Delivery (corporate)2010.0Contract ExpiredNaNNaNNaNNaNNaN...YesYesYesYesFemale26 30Temporary Full-timeAdministration (AO)1-21-2
16.341337e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Retirement-----...YesYesYesYesNaNNaNNaNNaNNaNNaN
26.341388e+17Mount Isa Institute of TAFEDelivery (teaching)2010.0Retirement-----...YesYesYesYesNaNNaNNaNNaNNaNNaN
36.341399e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Resignation-----...YesYesYesYesNaNNaNNaNNaNNaNNaN
46.341466e+17Southern Queensland Institute of TAFEDelivery (teaching)2010.0Resignation-Career Move - Private Sector---...YesYesYesYesMale41 45Permanent Full-timeTeacher (including LVT)3-43-4
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5 rows × 72 columns

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" ], "text/plain": [ " Record ID Institute \\\n", "0 6.341330e+17 Southern Queensland Institute of TAFE \n", "1 6.341337e+17 Mount Isa Institute of TAFE \n", "2 6.341388e+17 Mount Isa Institute of TAFE \n", "3 6.341399e+17 Mount Isa Institute of TAFE \n", "4 6.341466e+17 Southern Queensland Institute of TAFE \n", "\n", " WorkArea CESSATION YEAR Reason for ceasing employment \\\n", "0 Non-Delivery (corporate) 2010.0 Contract Expired \n", "1 Non-Delivery (corporate) 2010.0 Retirement \n", "2 Delivery (teaching) 2010.0 Retirement \n", "3 Non-Delivery (corporate) 2010.0 Resignation \n", "4 Delivery (teaching) 2010.0 Resignation \n", "\n", " Contributing Factors. Career Move - Public Sector \\\n", "0 NaN \n", "1 - \n", "2 - \n", "3 - \n", "4 - \n", "\n", " Contributing Factors. Career Move - Private Sector \\\n", "0 NaN \n", "1 - \n", "2 - \n", "3 - \n", "4 Career Move - Private Sector \n", "\n", " Contributing Factors. Career Move - Self-employment \\\n", "0 NaN \n", "1 - \n", "2 - \n", "3 - \n", "4 - \n", "\n", " Contributing Factors. Ill Health Contributing Factors. Maternity/Family \\\n", "0 NaN NaN \n", "1 - - \n", "2 - - \n", "3 - - \n", "4 - - \n", "\n", " ... \\\n", "0 ... \n", "1 ... \n", "2 ... \n", "3 ... \n", "4 ... \n", "\n", " Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 Yes \n", "4 Yes \n", "\n", " Workplace. Topic:Does your workplace promote and practice the principles of employment equity? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 Yes \n", "4 Yes \n", "\n", " Workplace. Topic:Does your workplace value the diversity of its employees? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 Yes \n", "4 Yes \n", "\n", " Workplace. Topic:Would you recommend the Institute as an employer to others? \\\n", "0 Yes \n", "1 Yes \n", "2 Yes \n", "3 Yes \n", "4 Yes \n", "\n", " Gender. What is your Gender? CurrentAge. Current Age \\\n", "0 Female 26 30 \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 Male 41 45 \n", "\n", " Employment Type. Employment Type Classification. Classification \\\n", "0 Temporary Full-time Administration (AO) \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 Permanent Full-time Teacher (including LVT) \n", "\n", " LengthofServiceOverall. Overall Length of Service at Institute (in years) \\\n", "0 1-2 \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 3-4 \n", "\n", " LengthofServiceCurrent. Length of Service at current workplace (in years) \n", "0 1-2 \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 3-4 \n", "\n", "[5 rows x 72 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tafe_survey.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 702 entries, 0 to 701\n", "Data columns (total 72 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Record ID 702 non-null float64\n", " 1 Institute 702 non-null object \n", " 2 WorkArea 702 non-null object \n", " 3 CESSATION YEAR 695 non-null float64\n", " 4 Reason for ceasing employment 701 non-null object \n", " 5 Contributing Factors. Career Move - Public Sector 437 non-null object \n", " 6 Contributing Factors. Career Move - Private Sector 437 non-null object \n", " 7 Contributing Factors. Career Move - Self-employment 437 non-null object \n", " 8 Contributing Factors. Ill Health 437 non-null object \n", " 9 Contributing Factors. Maternity/Family 437 non-null object \n", " 10 Contributing Factors. Dissatisfaction 437 non-null object \n", " 11 Contributing Factors. Job Dissatisfaction 437 non-null object \n", " 12 Contributing Factors. Interpersonal Conflict 437 non-null object \n", " 13 Contributing Factors. Study 437 non-null object \n", " 14 Contributing Factors. Travel 437 non-null object \n", " 15 Contributing Factors. Other 437 non-null object \n", " 16 Contributing Factors. NONE 437 non-null object \n", " 17 Main Factor. Which of these was the main factor for leaving? 113 non-null object \n", " 18 InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction 608 non-null object \n", " 19 InstituteViews. Topic:2. I was given access to skills training to help me do my job better 613 non-null object \n", " 20 InstituteViews. Topic:3. I was given adequate opportunities for personal development 610 non-null object \n", " 21 InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL% 608 non-null object \n", " 22 InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had 615 non-null object \n", " 23 InstituteViews. Topic:6. The organisation recognised when staff did good work 607 non-null object \n", " 24 InstituteViews. Topic:7. Management was generally supportive of me 614 non-null object \n", " 25 InstituteViews. Topic:8. Management was generally supportive of my team 608 non-null object \n", " 26 InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me 610 non-null object \n", " 27 InstituteViews. Topic:10. Staff morale was positive within the Institute 602 non-null object \n", " 28 InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly 601 non-null object \n", " 29 InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently 597 non-null object \n", " 30 InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly 601 non-null object \n", " 31 WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit 609 non-null object \n", " 32 WorkUnitViews. Topic:15. I worked well with my colleagues 605 non-null object \n", " 33 WorkUnitViews. Topic:16. My job was challenging and interesting 607 non-null object \n", " 34 WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work 610 non-null object \n", " 35 WorkUnitViews. Topic:18. I had sufficient contact with other people in my job 613 non-null object \n", " 36 WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job 609 non-null object \n", " 37 WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job 609 non-null object \n", " 38 WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT] 608 non-null object \n", " 39 WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job 608 non-null object \n", " 40 WorkUnitViews. Topic:23. My job provided sufficient variety 611 non-null object \n", " 41 WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job 610 non-null object \n", " 42 WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction 611 non-null object \n", " 43 WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance 606 non-null object \n", " 44 WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area 610 non-null object \n", " 45 WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date 609 non-null object \n", " 46 WorkUnitViews. Topic:29. There was adequate communication between staff in my unit 603 non-null object \n", " 47 WorkUnitViews. Topic:30. Staff morale was positive within my work unit 606 non-null object \n", " 48 Induction. Did you undertake Workplace Induction? 619 non-null object \n", " 49 InductionInfo. Topic:Did you undertake a Corporate Induction? 432 non-null object \n", " 50 InductionInfo. Topic:Did you undertake a Institute Induction? 483 non-null object \n", " 51 InductionInfo. Topic: Did you undertake Team Induction? 440 non-null object \n", " 52 InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object \n", " 53 InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted? 555 non-null object \n", " 54 InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction? 555 non-null object \n", " 55 InductionInfo. Face to Face Topic:Did you undertake a Institute Induction? 530 non-null object \n", " 56 InductionInfo. On-line Topic:Did you undertake a Institute Induction? 555 non-null object \n", " 57 InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction? 553 non-null object \n", " 58 InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category? 555 non-null object \n", " 59 InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.] 555 non-null object \n", " 60 InductionInfo. Induction Manual Topic: Did you undertake Team Induction? 555 non-null object \n", " 61 Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)? 608 non-null object \n", " 62 Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination? 594 non-null object \n", " 63 Workplace. Topic:Does your workplace promote and practice the principles of employment equity? 587 non-null object \n", " 64 Workplace. Topic:Does your workplace value the diversity of its employees? 586 non-null object \n", " 65 Workplace. Topic:Would you recommend the Institute as an employer to others? 581 non-null object \n", " 66 Gender. What is your Gender? 596 non-null object \n", " 67 CurrentAge. Current Age 596 non-null object \n", " 68 Employment Type. Employment Type 596 non-null object \n", " 69 Classification. Classification 596 non-null object \n", " 70 LengthofServiceOverall. Overall Length of Service at Institute (in years) 596 non-null object \n", " 71 LengthofServiceCurrent. Length of Service at current workplace (in years) 596 non-null object \n", "dtypes: float64(2), object(70)\n", "memory usage: 395.0+ KB\n" ] } ], "source": [ "tafe_survey.info()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Record ID 0\n", "Institute 0\n", "WorkArea 0\n", "CESSATION YEAR 7\n", "Reason for ceasing employment 1\n", " ... \n", "CurrentAge. Current Age 106\n", "Employment Type. Employment Type 106\n", "Classification. Classification 106\n", "LengthofServiceOverall. Overall Length of Service at Institute (in years) 106\n", "LengthofServiceCurrent. Length of Service at current workplace (in years) 106\n", "Length: 72, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tafe_survey.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "1. As some of the columns in both the columns are named different but imply the same, we might need to rename the columns so that there is order while combining the data and answering questions like:\n", "- Are employees who only worked for the institutes for a short period of time resigning due to some kind of dissatisfaction? What about employees who have been there longer?\n", "- Are younger employees resigning due to some kind of dissatisfaction? What about older employees?\n", "\n", "2. There are some cells in dete_survey which contain 'Not Stated' rather than Nan. While cleaning data we have to check uniformity in the columns, that is the values in the columns should have similar datatype and should basically belong to the same group as the other data. For example, if a column contains the marks of a student, we should make sure that there are no values which represent grades or percentage. \n", "\n", "\n", "3. There are many columns in both the surveys that are not needed for our analysis.\n", "\n", "\n", "\n", "4. The number of columns in both the surveys are not same, we will need to filter the useful columns.\n", " \n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# solving point 2 as missing values are marked by 'Not Stated'\n", "dete_survey = pd.read_csv('dete_survey.csv',na_values = 'Not Stated')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cleaning Column Names" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['id', 'separationtype', 'cease_date', 'dete_start_date',\n", " 'role_start_date', 'position', 'classification', 'region',\n", " 'business_unit', 'employment_status', 'career_move_to_public_sector',\n", " 'career_move_to_private_sector', 'interpersonal_conflicts',\n", " 'job_dissatisfaction', 'dissatisfaction_with_the_department',\n", " 'physical_work_environment', 'lack_of_recognition',\n", " 'lack_of_job_security', 'work_location', 'employment_conditions',\n", " 'maternity/family', 'relocation', 'study/travel', 'ill_health',\n", " 'traumatic_incident', 'work_life_balance', 'workload',\n", " 'none_of_the_above', 'professional_development',\n", " 'opportunities_for_promotion', 'staff_morale', 'workplace_issue',\n", " 'physical_environment', 'worklife_balance',\n", " 'stress_and_pressure_support', 'performance_of_supervisor',\n", " 'peer_support', 'initiative', 'skills', 'coach', 'career_aspirations',\n", " 'feedback', 'further_pd', 'communication', 'my_say', 'information',\n", " 'kept_informed', 'wellness_programs', 'health_&_safety', 'gender',\n", " 'age', 'aboriginal', 'torres_strait', 'south_sea', 'disability',\n", " 'nesb'],\n", " dtype='object')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# column names are more ordered in dete_survey so we will rename columns in tafe_survey according to dete_survey\n", "# first, we will uniform column names in dete_survey\n", "dete_survey.columns = dete_survey.columns.str.strip().str.lower().str.replace(' ','_')\n", "dete_survey.columns" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idseparation_typecease_datedete_start_daterole_start_datepositionclassificationregionbusiness_unitemployment_status...kept_informedwellness_programshealth_&_safetygenderageaboriginaltorres_straitsouth_seadisabilitynesb
01Ill Health Retirement08/20121984.02004.0Public ServantA01-A04Central OfficeCorporate Strategy and PeformancePermanent Full-time...NNNMale56-60NaNNaNNaNNaNYes
12Voluntary Early Retirement (VER)08/2012NaNNaNPublic ServantAO5-AO7Central OfficeCorporate Strategy and PeformancePermanent Full-time...NNNMale56-60NaNNaNNaNNaNNaN
23Voluntary Early Retirement (VER)05/20122011.02011.0Schools OfficerNaNCentral OfficeEducation QueenslandPermanent Full-time...NNNMale61 or olderNaNNaNNaNNaNNaN
34Resignation-Other reasons05/20122005.02006.0TeacherPrimaryCentral QueenslandNaNPermanent Full-time...ANAFemale36-40NaNNaNNaNNaNNaN
45Age Retirement05/20121970.01989.0Head of Curriculum/Head of Special EducationNaNSouth EastNaNPermanent Full-time...NAMFemale61 or olderNaNNaNNaNNaNNaN
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5 rows × 56 columns

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" ], "text/plain": [ " id separation_type cease_date dete_start_date \\\n", "0 1 Ill Health Retirement 08/2012 1984.0 \n", "1 2 Voluntary Early Retirement (VER) 08/2012 NaN \n", "2 3 Voluntary Early Retirement (VER) 05/2012 2011.0 \n", "3 4 Resignation-Other reasons 05/2012 2005.0 \n", "4 5 Age Retirement 05/2012 1970.0 \n", "\n", " role_start_date position \\\n", "0 2004.0 Public Servant \n", "1 NaN Public Servant \n", "2 2011.0 Schools Officer \n", "3 2006.0 Teacher \n", "4 1989.0 Head of Curriculum/Head of Special Education \n", "\n", " classification region business_unit \\\n", "0 A01-A04 Central Office Corporate Strategy and Peformance \n", "1 AO5-AO7 Central Office Corporate Strategy and Peformance \n", "2 NaN Central Office Education Queensland \n", "3 Primary Central Queensland NaN \n", "4 NaN South East NaN \n", "\n", " employment_status ... kept_informed wellness_programs \\\n", "0 Permanent Full-time ... N N \n", "1 Permanent Full-time ... N N \n", "2 Permanent Full-time ... N N \n", "3 Permanent Full-time ... A N \n", "4 Permanent Full-time ... N A \n", "\n", " health_&_safety gender age aboriginal torres_strait south_sea \\\n", "0 N Male 56-60 NaN NaN NaN \n", "1 N Male 56-60 NaN NaN NaN \n", "2 N Male 61 or older NaN NaN NaN \n", "3 A Female 36-40 NaN NaN NaN \n", "4 M Female 61 or older NaN NaN NaN \n", "\n", " disability nesb \n", "0 NaN Yes \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", "[5 rows x 56 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_survey.rename({'separationtype': 'separation_type'}, axis=1, inplace=True)\n", "dete_survey.head()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# renaming in tafe_survey\n", "columns = {'Record ID': 'id',\n", "'CESSATION YEAR':'cease_date',\n", "'Reason for ceasing employment':'separation_type',\n", "'Gender. What is your Gender?':'gender',\n", "'CurrentAge. Current Age':'age',\n", "'Employment Type. Employment Type':'employment_status',\n", "'Classification. Classification':'position',\n", "'LengthofServiceOverall. Overall Length of Service at Institute (in years)':'institute_service',\n", "'LengthofServiceCurrent. Length of Service at current workplace (in years)':'role_service',\n", "'Contributing Factors. Dissatisfaction':'factors_diss',\n", "'Contributing Factors. Job Dissatisfaction':'factors_job_diss'\n", " }\n", "tafe_survey = tafe_survey.rename(columns = columns)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['id', 'Institute', 'WorkArea', 'cease_date', 'separation_type',\n", " 'Contributing Factors. Career Move - Public Sector ',\n", " 'Contributing Factors. Career Move - Private Sector ',\n", " 'Contributing Factors. Career Move - Self-employment',\n", " 'Contributing Factors. Ill Health',\n", " 'Contributing Factors. Maternity/Family', 'factors_diss',\n", " 'factors_job_diss', 'Contributing Factors. Interpersonal Conflict',\n", " 'Contributing Factors. Study', 'Contributing Factors. Travel',\n", " 'Contributing Factors. Other', 'Contributing Factors. NONE',\n", " 'Main Factor. Which of these was the main factor for leaving?',\n", " 'InstituteViews. Topic:1. I feel the senior leadership had a clear vision and direction',\n", " 'InstituteViews. Topic:2. I was given access to skills training to help me do my job better',\n", " 'InstituteViews. Topic:3. I was given adequate opportunities for personal development',\n", " 'InstituteViews. Topic:4. I was given adequate opportunities for promotion within %Institute]Q25LBL%',\n", " 'InstituteViews. Topic:5. I felt the salary for the job was right for the responsibilities I had',\n", " 'InstituteViews. Topic:6. The organisation recognised when staff did good work',\n", " 'InstituteViews. Topic:7. Management was generally supportive of me',\n", " 'InstituteViews. Topic:8. Management was generally supportive of my team',\n", " 'InstituteViews. Topic:9. I was kept informed of the changes in the organisation which would affect me',\n", " 'InstituteViews. Topic:10. Staff morale was positive within the Institute',\n", " 'InstituteViews. Topic:11. If I had a workplace issue it was dealt with quickly',\n", " 'InstituteViews. Topic:12. If I had a workplace issue it was dealt with efficiently',\n", " 'InstituteViews. Topic:13. If I had a workplace issue it was dealt with discreetly',\n", " 'WorkUnitViews. Topic:14. I was satisfied with the quality of the management and supervision within my work unit',\n", " 'WorkUnitViews. Topic:15. I worked well with my colleagues',\n", " 'WorkUnitViews. Topic:16. My job was challenging and interesting',\n", " 'WorkUnitViews. Topic:17. I was encouraged to use my initiative in the course of my work',\n", " 'WorkUnitViews. Topic:18. I had sufficient contact with other people in my job',\n", " 'WorkUnitViews. Topic:19. I was given adequate support and co-operation by my peers to enable me to do my job',\n", " 'WorkUnitViews. Topic:20. I was able to use the full range of my skills in my job',\n", " 'WorkUnitViews. Topic:21. I was able to use the full range of my abilities in my job. ; Category:Level of Agreement; Question:YOUR VIEWS ABOUT YOUR WORK UNIT]',\n", " 'WorkUnitViews. Topic:22. I was able to use the full range of my knowledge in my job',\n", " 'WorkUnitViews. Topic:23. My job provided sufficient variety',\n", " 'WorkUnitViews. Topic:24. I was able to cope with the level of stress and pressure in my job',\n", " 'WorkUnitViews. Topic:25. My job allowed me to balance the demands of work and family to my satisfaction',\n", " 'WorkUnitViews. Topic:26. My supervisor gave me adequate personal recognition and feedback on my performance',\n", " 'WorkUnitViews. Topic:27. My working environment was satisfactory e.g. sufficient space, good lighting, suitable seating and working area',\n", " 'WorkUnitViews. Topic:28. I was given the opportunity to mentor and coach others in order for me to pass on my skills and knowledge prior to my cessation date',\n", " 'WorkUnitViews. Topic:29. There was adequate communication between staff in my unit',\n", " 'WorkUnitViews. Topic:30. Staff morale was positive within my work unit',\n", " 'Induction. Did you undertake Workplace Induction?',\n", " 'InductionInfo. Topic:Did you undertake a Corporate Induction?',\n", " 'InductionInfo. Topic:Did you undertake a Institute Induction?',\n", " 'InductionInfo. Topic: Did you undertake Team Induction?',\n", " 'InductionInfo. Face to Face Topic:Did you undertake a Corporate Induction; Category:How it was conducted?',\n", " 'InductionInfo. On-line Topic:Did you undertake a Corporate Induction; Category:How it was conducted?',\n", " 'InductionInfo. Induction Manual Topic:Did you undertake a Corporate Induction?',\n", " 'InductionInfo. Face to Face Topic:Did you undertake a Institute Induction?',\n", " 'InductionInfo. On-line Topic:Did you undertake a Institute Induction?',\n", " 'InductionInfo. Induction Manual Topic:Did you undertake a Institute Induction?',\n", " 'InductionInfo. Face to Face Topic: Did you undertake Team Induction; Category?',\n", " 'InductionInfo. On-line Topic: Did you undertake Team Induction?process you undertook and how it was conducted.]',\n", " 'InductionInfo. Induction Manual Topic: Did you undertake Team Induction?',\n", " 'Workplace. Topic:Did you and your Manager develop a Performance and Professional Development Plan (PPDP)?',\n", " 'Workplace. Topic:Does your workplace promote a work culture free from all forms of unlawful discrimination?',\n", " 'Workplace. Topic:Does your workplace promote and practice the principles of employment equity?',\n", " 'Workplace. Topic:Does your workplace value the diversity of its employees?',\n", " 'Workplace. Topic:Would you recommend the Institute as an employer to others?',\n", " 'gender', 'age', 'employment_status', 'position', 'institute_service',\n", " 'role_service'],\n", " dtype='object')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tafe_survey.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dropping Columns" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# now we will try to delete some columns we dont need\n", "# columns 28-48 have denoted values with no particular meaning or there is no guide to weigh them\n", "# hence it is of no use to us\n", "# the last 5 columns in dete_survey is dominated by null values\n", "dete_survey = dete_survey.drop(dete_survey.columns[-5:], axis = 1)\n", "dete_survey = dete_survey.drop(dete_survey.columns[28:49], axis = 1)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# we choose columns to drop on the same basis as we did for dete_survey\n", "tafe_survey = tafe_survey.drop(tafe_survey.columns[17:66], axis = 1)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idseparation_typecease_datedete_start_daterole_start_datepositionclassificationregionbusiness_unitemployment_status...maternity/familyrelocationstudy/travelill_healthtraumatic_incidentwork_life_balanceworkloadnone_of_the_abovegenderage
01Ill Health Retirement08/20121984.02004.0Public ServantA01-A04Central OfficeCorporate Strategy and PeformancePermanent Full-time...FalseFalseFalseFalseFalseFalseFalseTrueMale56-60
12Voluntary Early Retirement (VER)08/2012NaNNaNPublic ServantAO5-AO7Central OfficeCorporate Strategy and PeformancePermanent Full-time...FalseFalseFalseFalseFalseFalseFalseFalseMale56-60
23Voluntary Early Retirement (VER)05/20122011.02011.0Schools OfficerNaNCentral OfficeEducation QueenslandPermanent Full-time...FalseFalseFalseFalseFalseFalseFalseTrueMale61 or older
34Resignation-Other reasons05/20122005.02006.0TeacherPrimaryCentral QueenslandNaNPermanent Full-time...FalseFalseFalseFalseFalseFalseFalseFalseFemale36-40
45Age Retirement05/20121970.01989.0Head of Curriculum/Head of Special EducationNaNSouth EastNaNPermanent Full-time...FalseFalseFalseFalseFalseTrueFalseFalseFemale61 or older
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5 rows × 30 columns

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" ], "text/plain": [ " id separation_type cease_date dete_start_date \\\n", "0 1 Ill Health Retirement 08/2012 1984.0 \n", "1 2 Voluntary Early Retirement (VER) 08/2012 NaN \n", "2 3 Voluntary Early Retirement (VER) 05/2012 2011.0 \n", "3 4 Resignation-Other reasons 05/2012 2005.0 \n", "4 5 Age Retirement 05/2012 1970.0 \n", "\n", " role_start_date position \\\n", "0 2004.0 Public Servant \n", "1 NaN Public Servant \n", "2 2011.0 Schools Officer \n", "3 2006.0 Teacher \n", "4 1989.0 Head of Curriculum/Head of Special Education \n", "\n", " classification region business_unit \\\n", "0 A01-A04 Central Office Corporate Strategy and Peformance \n", "1 AO5-AO7 Central Office Corporate Strategy and Peformance \n", "2 NaN Central Office Education Queensland \n", "3 Primary Central Queensland NaN \n", "4 NaN South East NaN \n", "\n", " employment_status ... maternity/family relocation study/travel \\\n", "0 Permanent Full-time ... False False False \n", "1 Permanent Full-time ... False False False \n", "2 Permanent Full-time ... False False False \n", "3 Permanent Full-time ... False False False \n", "4 Permanent Full-time ... False False False \n", "\n", " ill_health traumatic_incident work_life_balance workload \\\n", "0 False False False False \n", "1 False False False False \n", "2 False False False False \n", "3 False False False False \n", "4 False False True False \n", "\n", " none_of_the_above gender age \n", "0 True Male 56-60 \n", "1 False Male 56-60 \n", "2 True Male 61 or older \n", "3 False Female 36-40 \n", "4 False Female 61 or older \n", "\n", "[5 rows x 30 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_survey.head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idInstituteWorkAreacease_dateseparation_typeContributing Factors. Career Move - Public SectorContributing Factors. Career Move - Private SectorContributing Factors. Career Move - Self-employmentContributing Factors. Ill HealthContributing Factors. Maternity/Family...Contributing Factors. StudyContributing Factors. TravelContributing Factors. OtherContributing Factors. NONEgenderageemployment_statuspositioninstitute_servicerole_service
06.341330e+17Southern Queensland Institute of TAFENon-Delivery (corporate)2010.0Contract ExpiredNaNNaNNaNNaNNaN...NaNNaNNaNNaNFemale26 30Temporary Full-timeAdministration (AO)1-21-2
16.341337e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Retirement-----...-Travel--NaNNaNNaNNaNNaNNaN
26.341388e+17Mount Isa Institute of TAFEDelivery (teaching)2010.0Retirement-----...---NONENaNNaNNaNNaNNaNNaN
36.341399e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Resignation-----...-Travel--NaNNaNNaNNaNNaNNaN
46.341466e+17Southern Queensland Institute of TAFEDelivery (teaching)2010.0Resignation-Career Move - Private Sector---...----Male41 45Permanent Full-timeTeacher (including LVT)3-43-4
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5 rows × 23 columns

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" ], "text/plain": [ " id Institute \\\n", "0 6.341330e+17 Southern Queensland Institute of TAFE \n", "1 6.341337e+17 Mount Isa Institute of TAFE \n", "2 6.341388e+17 Mount Isa Institute of TAFE \n", "3 6.341399e+17 Mount Isa Institute of TAFE \n", "4 6.341466e+17 Southern Queensland Institute of TAFE \n", "\n", " WorkArea cease_date separation_type \\\n", "0 Non-Delivery (corporate) 2010.0 Contract Expired \n", "1 Non-Delivery (corporate) 2010.0 Retirement \n", "2 Delivery (teaching) 2010.0 Retirement \n", "3 Non-Delivery (corporate) 2010.0 Resignation \n", "4 Delivery (teaching) 2010.0 Resignation \n", "\n", " Contributing Factors. Career Move - Public Sector \\\n", "0 NaN \n", "1 - \n", "2 - \n", "3 - \n", "4 - \n", "\n", " Contributing Factors. Career Move - Private Sector \\\n", "0 NaN \n", "1 - \n", "2 - \n", "3 - \n", "4 Career Move - Private Sector \n", "\n", " Contributing Factors. Career Move - Self-employment \\\n", "0 NaN \n", "1 - \n", "2 - \n", "3 - \n", "4 - \n", "\n", " Contributing Factors. Ill Health Contributing Factors. Maternity/Family \\\n", "0 NaN NaN \n", "1 - - \n", "2 - - \n", "3 - - \n", "4 - - \n", "\n", " ... Contributing Factors. Study Contributing Factors. Travel \\\n", "0 ... NaN NaN \n", "1 ... - Travel \n", "2 ... - - \n", "3 ... - Travel \n", "4 ... - - \n", "\n", " Contributing Factors. Other Contributing Factors. NONE gender age \\\n", "0 NaN NaN Female 26 30 \n", "1 - - NaN NaN \n", "2 - NONE NaN NaN \n", "3 - - NaN NaN \n", "4 - - Male 41 45 \n", "\n", " employment_status position institute_service role_service \n", "0 Temporary Full-time Administration (AO) 1-2 1-2 \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 Permanent Full-time Teacher (including LVT) 3-4 3-4 \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tafe_survey.head()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 822\n", "dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# checking for duplicates by id\n", "dete_survey.duplicated(['id']).value_counts()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 702\n", "dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tafe_survey.duplicated(['id']).value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Filtering (Resigned Employees)\n", "\n", "For the development of this project we only need to focus on those people who resigned, that is why we'll filter both datasets taking as into account those values in the column separation_type that contain the word 'Resignation'." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Age Retirement 285\n", "Resignation-Other reasons 150\n", "Resignation-Other employer 91\n", "Resignation-Move overseas/interstate 70\n", "Voluntary Early Retirement (VER) 67\n", "Ill Health Retirement 61\n", "Other 49\n", "Contract Expired 34\n", "Termination 15\n", "Name: separation_type, dtype: int64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_survey['separation_type'].value_counts(dropna = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Separation due to all resignation types is around 53% followed by Age Retirement at around 35%. Now we will see that resignation in tafe_survey is just less than 50 %. The high proportions of resignation types needs some answers which we will try to answer after preparing our data." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Resignation 340\n", "Contract Expired 127\n", "Retrenchment/ Redundancy 104\n", "Retirement 82\n", "Transfer 25\n", "Termination 23\n", "NaN 1\n", "Name: separation_type, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tafe_survey['separation_type'].value_counts(dropna = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the DETE-Survey dataset we see that in the column separation_type there are 3 values containing the word 'Resignation'. Whereas, in the TAFE-Survey dataset, there is only one." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# filtering out separation due to resignation\n", "dete_survey['separationtype'] = dete_survey['separation_type'].str.split('-').str[0]\n", "dete_resignations = dete_survey.copy()[dete_survey['separationtype'].str.contains(r'Resignation')]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idInstituteWorkAreacease_dateseparation_typeContributing Factors. Career Move - Public SectorContributing Factors. Career Move - Private SectorContributing Factors. Career Move - Self-employmentContributing Factors. Ill HealthContributing Factors. Maternity/Family...Contributing Factors. StudyContributing Factors. TravelContributing Factors. OtherContributing Factors. NONEgenderageemployment_statuspositioninstitute_servicerole_service
36.341399e+17Mount Isa Institute of TAFENon-Delivery (corporate)2010.0Resignation-----...-Travel--NaNNaNNaNNaNNaNNaN
46.341466e+17Southern Queensland Institute of TAFEDelivery (teaching)2010.0Resignation-Career Move - Private Sector---...----Male41 45Permanent Full-timeTeacher (including LVT)3-43-4
56.341475e+17Southern Queensland Institute of TAFEDelivery (teaching)2010.0Resignation-----...--Other-Female56 or olderContract/casualTeacher (including LVT)7-107-10
66.341520e+17Barrier Reef Institute of TAFENon-Delivery (corporate)2010.0Resignation-Career Move - Private Sector--Maternity/Family...--Other-Male20 or youngerTemporary Full-timeAdministration (AO)3-43-4
76.341537e+17Southern Queensland Institute of TAFEDelivery (teaching)2010.0Resignation-----...--Other-Male46 50Permanent Full-timeTeacher (including LVT)3-43-4
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5 rows × 23 columns

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" ], "text/plain": [ " id Institute \\\n", "3 6.341399e+17 Mount Isa Institute of TAFE \n", "4 6.341466e+17 Southern Queensland Institute of TAFE \n", "5 6.341475e+17 Southern Queensland Institute of TAFE \n", "6 6.341520e+17 Barrier Reef Institute of TAFE \n", "7 6.341537e+17 Southern Queensland Institute of TAFE \n", "\n", " WorkArea cease_date separation_type \\\n", "3 Non-Delivery (corporate) 2010.0 Resignation \n", "4 Delivery (teaching) 2010.0 Resignation \n", "5 Delivery (teaching) 2010.0 Resignation \n", "6 Non-Delivery (corporate) 2010.0 Resignation \n", "7 Delivery (teaching) 2010.0 Resignation \n", "\n", " Contributing Factors. Career Move - Public Sector \\\n", "3 - \n", "4 - \n", "5 - \n", "6 - \n", "7 - \n", "\n", " Contributing Factors. Career Move - Private Sector \\\n", "3 - \n", "4 Career Move - Private Sector \n", "5 - \n", "6 Career Move - Private Sector \n", "7 - \n", "\n", " Contributing Factors. Career Move - Self-employment \\\n", "3 - \n", "4 - \n", "5 - \n", "6 - \n", "7 - \n", "\n", " Contributing Factors. Ill Health Contributing Factors. Maternity/Family \\\n", "3 - - \n", "4 - - \n", "5 - - \n", "6 - Maternity/Family \n", "7 - - \n", "\n", " ... Contributing Factors. Study Contributing Factors. Travel \\\n", "3 ... - Travel \n", "4 ... - - \n", "5 ... - - \n", "6 ... - - \n", "7 ... - - \n", "\n", " Contributing Factors. Other Contributing Factors. NONE gender \\\n", "3 - - NaN \n", "4 - - Male \n", "5 Other - Female \n", "6 Other - Male \n", "7 Other - Male \n", "\n", " age employment_status position \\\n", "3 NaN NaN NaN \n", "4 41 45 Permanent Full-time Teacher (including LVT) \n", "5 56 or older Contract/casual Teacher (including LVT) \n", "6 20 or younger Temporary Full-time Administration (AO) \n", "7 46 50 Permanent Full-time Teacher (including LVT) \n", "\n", " institute_service role_service \n", "3 NaN NaN \n", "4 3-4 3-4 \n", "5 7-10 7-10 \n", "6 3-4 3-4 \n", "7 3-4 3-4 \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tafe_resignations = tafe_survey.copy()[tafe_survey['separation_type'].str.contains(r'Resignation',na=False)]\n", "tafe_resignations.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Verification / Checking Validity\n", "\n", "Now we will check the validity of dates and years in our dataset.\n", "Starting with `cease_date`:, the last year of a person's employment should not be more than the current year(the year the data was created). Similarly,for `date_start_date` , we can say that people working here wont be over 60, and reasoning that they started working their in their 20's, we can say that the least value for this column can be around\n", "1935.\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2012 126\n", "2013 74\n", "01/2014 22\n", "12/2013 17\n", "06/2013 14\n", "09/2013 11\n", "11/2013 9\n", "07/2013 9\n", "10/2013 6\n", "08/2013 4\n", "05/2013 2\n", "05/2012 2\n", "09/2010 1\n", "07/2006 1\n", "07/2012 1\n", "2010 1\n", "Name: cease_date, dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_resignations['cease_date'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is what we discussed about [here](#section)." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2013.0 146\n", "2012.0 129\n", "2014.0 22\n", "2010.0 2\n", "2006.0 1\n", "Name: cease_date, dtype: int64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# cleaning the column by extracting the years\n", "dete_resignations['cease_date'] = dete_resignations['cease_date'].str.extract(r'(?P[1-2][0-9]{3})')\n", "dete_resignations['cease_date'] = dete_resignations['cease_date'].astype(float)\n", "dete_resignations['cease_date'].value_counts()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2011.0 24\n", "2008.0 22\n", "2007.0 21\n", "2012.0 21\n", "2010.0 17\n", "2005.0 15\n", "2004.0 14\n", "2009.0 13\n", "2006.0 13\n", "2013.0 10\n", "2000.0 9\n", "1999.0 8\n", "1996.0 6\n", "2002.0 6\n", "1992.0 6\n", "1998.0 6\n", "2003.0 6\n", "1994.0 6\n", "1993.0 5\n", "1990.0 5\n", "1980.0 5\n", "1997.0 5\n", "1991.0 4\n", "1989.0 4\n", "1988.0 4\n", "1995.0 4\n", "2001.0 3\n", "1985.0 3\n", "1986.0 3\n", "1983.0 2\n", "1976.0 2\n", "1974.0 2\n", "1971.0 1\n", "1972.0 1\n", "1984.0 1\n", "1982.0 1\n", "1987.0 1\n", "1975.0 1\n", "1973.0 1\n", "1977.0 1\n", "1963.0 1\n", "Name: dete_start_date, dtype: int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_resignations['dete_start_date'] = dete_resignations['dete_start_date'].astype(float)\n", "dete_resignations['dete_start_date'].value_counts()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2011.0 116\n", "2012.0 94\n", "2010.0 68\n", "2013.0 55\n", "2009.0 2\n", "Name: cease_date, dtype: int64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# no need to change or extract anyhtin here \n", "tafe_resignations['cease_date'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have verified the date columns and none of the dates has been violating our assumptions. We needed to verify this as we are to going to sort or filter the employees based on their experience." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating Experience/ Service Time\n", "\n", "It is noiticable that the tafe_resignations dataframe already contains a \"service\" column, which we renamed to institute_service. In order to analyze both surveys together, we'll have to create a corresponding institute_service column in dete_resignations." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ " 5.0 23\n", " 1.0 22\n", " 3.0 20\n", "-0.0 20\n", " 6.0 17\n", " 4.0 16\n", " 9.0 14\n", " 2.0 14\n", " 7.0 13\n", " 13.0 8\n", " 8.0 8\n", " 20.0 7\n", " 15.0 7\n", " 10.0 6\n", " 22.0 6\n", " 14.0 6\n", " 17.0 6\n", " 12.0 6\n", " 16.0 5\n", " 18.0 5\n", " 23.0 4\n", " 11.0 4\n", " 24.0 4\n", " 39.0 3\n", " 19.0 3\n", " 21.0 3\n", " 32.0 3\n", " 28.0 2\n", " 26.0 2\n", " 25.0 2\n", " 30.0 2\n", " 36.0 2\n", " 29.0 1\n", " 33.0 1\n", " 42.0 1\n", " 27.0 1\n", " 41.0 1\n", " 35.0 1\n", " 38.0 1\n", " 34.0 1\n", " 49.0 1\n", " 31.0 1\n", "Name: institute_service, dtype: int64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_resignations['institute_service'] = (dete_resignations['dete_start_date'].astype(float)-dete_resignations['cease_date'].astype(float))*(-1)\n", "dete_resignations['institute_service'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Excluding the null values of the institute_service field from the DETE dataset, we observe that 42% of the employees worked at most 5 years." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "38" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_resignations['institute_service'].isnull().sum()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Less than 1 year 73\n", "1-2 64\n", "3-4 63\n", "NaN 50\n", "5-6 33\n", "11-20 26\n", "7-10 21\n", "More than 20 years 10\n", "Name: institute_service, dtype: int64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tafe_resignations['institute_service'].value_counts(dropna = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see from the values above, employee with relatively less experience have resigned in higher proportion.\n", "- Excluding null values, 47% of the employees resignation from TAFE had worked for less than 2 years." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Also, when we combine the datasets, institute_service in TAFE should have the same format as in DETE. We will deal with this later in tis notebook." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After a bit of cleaning and preparing our data for analysis, now we will have to classify employees as dissatsfied and then filter them according to their experience to answer our question." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Identification of Dissatisfied Employees\n", "\n", "Also, we will work with dete_resignations and tafe_resignations as the dataset has data corresponding to resigned employees only.\n", "In dete_resignations, I think the following columns contribute to the employee's decision:\n", "- 13 Job dissatisfaction \n", "- 14 Dissatisfaction with the department\n", "- 15 Physical work environment \n", "- 16 Lack of recognition \n", "- 17 Lack of job security \n", "- 18 Work location \n", "- 19 Employment conditions \n", "- 20 Maternity/family \n", "- 21 Relocation \n", "- 22 Study/Travel \n", "- 23 Ill Health \n", "- 24 Traumatic incident \n", "- 25 Work life balance \n", "- 26 Workload" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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311 rows × 14 columns

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" ], "text/plain": [ " job_dissatisfaction dissatisfaction_with_the_department \\\n", "3 False False \n", "5 False False \n", "8 False False \n", "9 True True \n", "11 False False \n", ".. ... ... \n", "808 False False \n", "815 False False \n", "816 False False \n", "819 False False \n", "821 False False \n", "\n", " physical_work_environment lack_of_recognition lack_of_job_security \\\n", "3 False False False \n", "5 False False False \n", "8 False False False \n", "9 False False False \n", "11 False False False \n", ".. ... ... ... \n", "808 False False False \n", "815 False False False \n", "816 False False False \n", "819 False False False \n", "821 False False False \n", "\n", " work_location employment_conditions maternity/family relocation \\\n", "3 False False False False \n", "5 False True True False \n", "8 False False False False \n", "9 False False False False \n", "11 False False True True \n", ".. ... ... ... ... \n", "808 False False True False \n", "815 False False False False \n", "816 False False False False \n", "819 False False True True \n", "821 False False False False \n", "\n", " study/travel ill_health traumatic_incident work_life_balance workload \n", "3 False False False False False \n", "5 False False False False False \n", "8 False False False False False \n", "9 False False False False False \n", "11 False False False False False \n", ".. ... ... ... ... ... \n", "808 False False False False False \n", "815 False False False False False \n", "816 True False False False False \n", "819 False False False True False \n", "821 False False False False False \n", "\n", "[311 rows x 14 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_resignations.loc[:,'job_dissatisfaction':'workload']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "So since these columns have boolean input values, even if one of the column to a corresponding row states false, then that employee will be classified as 'dissatisfied'.\n", "Before proceeding, I think we should not include columns such from maternity/family to traumatic_incident in columns such as dissatisfied as they are not related to the institute." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 162\n", "True 149\n", "Name: dissatisfied, dtype: int64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we will create a column 'dissatisfied' which will be of Boolean type\n", "# the role of 'any' is to return whether any element is True\n", "def update_vals(x):\n", " if x == '-':\n", " return False\n", " elif pd.isnull(x):\n", " return np.nan\n", " else:\n", " return True\n", "dete_resignations['dissatisfied'] = dete_resignations[['job_dissatisfaction',\n", " 'dissatisfaction_with_the_department', 'physical_work_environment',\n", " 'lack_of_recognition', 'lack_of_job_security', 'work_location',\n", " 'employment_conditions', 'work_life_balance',\n", " 'workload']].any(1,skipna=False)\n", "dete_resignations_up = dete_resignations.copy()\n", "dete_resignations_up['dissatisfied'].value_counts(dropna = False)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3 False\n", "5 True\n", "8 False\n", "9 True\n", "11 False\n", "Name: dissatisfied, dtype: bool" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_resignations_up['dissatisfied'].head()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 162\n", "True 149\n", "Name: dissatisfied, dtype: int64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dete_resignations_up['dissatisfied'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly for tefe_resignations we will include two columns:\n", "- Contributing Factors. Dissatisfaction\n", "- Contributing Factors. Job Dissatisfaction" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 241\n", "True 91\n", "NaN 8\n", "Name: dissatisfied, dtype: int64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def update_vals(x):\n", " if x == '-':\n", " return False\n", " elif pd.isnull(x):\n", " return np.nan\n", " else:\n", " return True\n", " \n", "tafe_resignations['dissatisfied'] = tafe_resignations[['factors_diss','factors_job_diss']].applymap(update_vals).any(1,skipna=False)\n", "tafe_resignations['dissatisfied'].head()\n", "tafe_resignations_up = tafe_resignations.copy()\n", "tafe_resignations_up['dissatisfied'].value_counts(dropna = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combining DataFrames\n", "\n", "We have performed various actions to clean and filter our data. Now we will be reeady to merge iot. Also, as we practiced in some lessons before, while merging it is better that each dataset has it as own identity. We have given an identity to each dataste by dedicating a column with their title." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "dete_resignations_up['institute'] = 'DETE'\n", "tafe_resignations_up['institute'] = 'TAFE'" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(651, 49)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# combining\n", "combined = pd.concat([dete_resignations_up, tafe_resignations_up], ignore_index=True)\n", "combined.shape" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "id 0\n", "separation_type 0\n", "cease_date 16\n", "dete_start_date 368\n", "role_start_date 380\n", "position 53\n", "classification 490\n", "region 386\n", "business_unit 619\n", "employment_status 54\n", "career_move_to_public_sector 340\n", "career_move_to_private_sector 340\n", "interpersonal_conflicts 340\n", "job_dissatisfaction 340\n", "dissatisfaction_with_the_department 340\n", "physical_work_environment 340\n", "lack_of_recognition 340\n", "lack_of_job_security 340\n", "work_location 340\n", "employment_conditions 340\n", "maternity/family 340\n", "relocation 340\n", "study/travel 340\n", "ill_health 340\n", "traumatic_incident 340\n", "work_life_balance 340\n", "workload 340\n", "none_of_the_above 340\n", "gender 59\n", "age 55\n", "separationtype 340\n", "institute_service 88\n", "dissatisfied 8\n", "institute 0\n", "Institute 311\n", "WorkArea 311\n", "Contributing Factors. Career Move - Public Sector 319\n", "Contributing Factors. Career Move - Private Sector 319\n", "Contributing Factors. Career Move - Self-employment 319\n", "Contributing Factors. Ill Health 319\n", "Contributing Factors. Maternity/Family 319\n", "factors_diss 319\n", "factors_job_diss 319\n", "Contributing Factors. Interpersonal Conflict 319\n", "Contributing Factors. Study 319\n", "Contributing Factors. Travel 319\n", "Contributing Factors. Other 319\n", "Contributing Factors. NONE 319\n", "role_service 361\n", "dtype: int64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined_null = combined.isnull().sum()\n", "combined_null" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "# we will drop the following columns \n", "columns_drop = combined_null[combined_null >= 400]" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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04.000000e+00Resignation-Other reasons2012.02005.02006.0TeacherCentral QueenslandPermanent Full-timeFalseTrue...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
16.000000e+00Resignation-Other reasons2012.01994.01997.0Guidance OfficerCentral OfficePermanent Full-timeFalseTrue...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
29.000000e+00Resignation-Other reasons2012.02009.02009.0TeacherNorth QueenslandPermanent Full-timeFalseTrue...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31.000000e+01Resignation-Other employer2012.01997.02008.0Teacher AideNaNPermanent Part-timeFalseFalse...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
41.200000e+01Resignation-Move overseas/interstate2012.02009.02009.0TeacherFar North QueenslandPermanent Full-timeFalseFalse...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
..................................................................
6466.350660e+17Resignation2013.0NaNNaNOperational (OO)NaNTemporary Full-timeNaNNaN...---------5-6
6476.350668e+17Resignation2013.0NaNNaNTeacher (including LVT)NaNTemporary Full-timeNaNNaN...---------1-2
6486.350677e+17Resignation2013.0NaNNaNNaNNaNNaNNaNNaN...---------NaN
6496.350704e+17Resignation2013.0NaNNaNTeacher (including LVT)NaNPermanent Full-timeNaNNaN...-------Other-1-2
6506.350730e+17Resignation2013.0NaNNaNAdministration (AO)NaNContract/casualNaNNaN...------Travel--1-2
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651 rows × 47 columns

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" ], "text/plain": [ " id separation_type cease_date \\\n", "0 4.000000e+00 Resignation-Other reasons 2012.0 \n", "1 6.000000e+00 Resignation-Other reasons 2012.0 \n", "2 9.000000e+00 Resignation-Other reasons 2012.0 \n", "3 1.000000e+01 Resignation-Other employer 2012.0 \n", "4 1.200000e+01 Resignation-Move overseas/interstate 2012.0 \n", ".. ... ... ... \n", "646 6.350660e+17 Resignation 2013.0 \n", "647 6.350668e+17 Resignation 2013.0 \n", "648 6.350677e+17 Resignation 2013.0 \n", "649 6.350704e+17 Resignation 2013.0 \n", "650 6.350730e+17 Resignation 2013.0 \n", "\n", " dete_start_date role_start_date position \\\n", "0 2005.0 2006.0 Teacher \n", "1 1994.0 1997.0 Guidance Officer \n", "2 2009.0 2009.0 Teacher \n", "3 1997.0 2008.0 Teacher Aide \n", "4 2009.0 2009.0 Teacher \n", ".. ... ... ... \n", "646 NaN NaN Operational (OO) \n", "647 NaN NaN Teacher (including LVT) \n", "648 NaN NaN NaN \n", "649 NaN NaN Teacher (including LVT) \n", "650 NaN NaN Administration (AO) \n", "\n", " region employment_status career_move_to_public_sector \\\n", "0 Central Queensland Permanent Full-time False \n", "1 Central Office Permanent Full-time False \n", "2 North Queensland Permanent Full-time False \n", "3 NaN Permanent Part-time False \n", "4 Far North Queensland Permanent Full-time False \n", ".. ... ... ... \n", "646 NaN Temporary Full-time NaN \n", "647 NaN Temporary Full-time NaN \n", "648 NaN NaN NaN \n", "649 NaN Permanent Full-time NaN \n", "650 NaN Contract/casual NaN \n", "\n", " career_move_to_private_sector ... Contributing Factors. Ill Health \\\n", "0 True ... NaN \n", "1 True ... NaN \n", "2 True ... NaN \n", "3 False ... NaN \n", "4 False ... NaN \n", ".. ... ... ... \n", "646 NaN ... - \n", "647 NaN ... - \n", "648 NaN ... - \n", "649 NaN ... - \n", "650 NaN ... - \n", "\n", " Contributing Factors. Maternity/Family factors_diss factors_job_diss \\\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", "646 - - - \n", "647 - - - \n", "648 - - - \n", "649 - - - \n", "650 - - - \n", "\n", " Contributing Factors. Interpersonal Conflict Contributing Factors. Study \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", ".. ... ... \n", "646 - - \n", "647 - - \n", "648 - - \n", "649 - - \n", "650 - - \n", "\n", " Contributing Factors. Travel Contributing Factors. Other \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", ".. ... ... \n", "646 - - \n", "647 - - \n", "648 - - \n", "649 - Other \n", "650 Travel - \n", "\n", " Contributing Factors. NONE role_service \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", ".. ... ... \n", "646 - 5-6 \n", "647 - 1-2 \n", "648 - NaN \n", "649 - 1-2 \n", "650 - 1-2 \n", "\n", "[651 rows x 47 columns]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined = combined.dropna(thresh = 200, axis = 1)\n", "combined" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 7\n", "1 18\n", "2 3\n", "3 15\n", "4 3\n", " ... \n", "646 5-6\n", "647 1-2\n", "648 NaN\n", "649 5-6\n", "650 3-4\n", "Name: institute_service, Length: 651, dtype: object" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined['institute_service']" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "float" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# the column is pretty inconsistent with its value(due to tafe_survey)\n", "type(combined['institute_service'][2])" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# column consists of diffeent dtypes, so direct string operation is impossible\n", "type(combined['institute_service'][640])" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idseparation_typecease_datedete_start_daterole_start_datepositionregionemployment_statuscareer_move_to_public_sectorcareer_move_to_private_sector...Contributing Factors. Ill HealthContributing Factors. Maternity/Familyfactors_dissfactors_job_dissContributing Factors. Interpersonal ConflictContributing Factors. StudyContributing Factors. TravelContributing Factors. OtherContributing Factors. NONErole_service
04.0Resignation-Other reasons2012.02005.02006.0TeacherCentral QueenslandPermanent Full-timeFalseTrue...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
16.0Resignation-Other reasons2012.01994.01997.0Guidance OfficerCentral OfficePermanent Full-timeFalseTrue...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
29.0Resignation-Other reasons2012.02009.02009.0TeacherNorth QueenslandPermanent Full-timeFalseTrue...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
310.0Resignation-Other employer2012.01997.02008.0Teacher AideNaNPermanent Part-timeFalseFalse...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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5 rows × 47 columns

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" ], "text/plain": [ " id separation_type cease_date dete_start_date \\\n", "0 4.0 Resignation-Other reasons 2012.0 2005.0 \n", "1 6.0 Resignation-Other reasons 2012.0 1994.0 \n", "2 9.0 Resignation-Other reasons 2012.0 2009.0 \n", "3 10.0 Resignation-Other employer 2012.0 1997.0 \n", "4 12.0 Resignation-Move overseas/interstate 2012.0 2009.0 \n", "\n", " role_start_date position region \\\n", "0 2006.0 Teacher Central Queensland \n", "1 1997.0 Guidance Officer Central Office \n", "2 2009.0 Teacher North Queensland \n", "3 2008.0 Teacher Aide NaN \n", "4 2009.0 Teacher Far North Queensland \n", "\n", " employment_status career_move_to_public_sector \\\n", "0 Permanent Full-time False \n", "1 Permanent Full-time False \n", "2 Permanent Full-time False \n", "3 Permanent Part-time False \n", "4 Permanent Full-time False \n", "\n", " career_move_to_private_sector ... Contributing Factors. Ill Health \\\n", "0 True ... NaN \n", "1 True ... NaN \n", "2 True ... NaN \n", "3 False ... NaN \n", "4 False ... NaN \n", "\n", " Contributing Factors. Maternity/Family factors_diss factors_job_diss \\\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", " Contributing Factors. Interpersonal Conflict Contributing Factors. Study \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " Contributing Factors. Travel Contributing Factors. Other \\\n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " Contributing Factors. NONE role_service \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", "[5 rows x 47 columns]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Classifying Employees by Length of Service\n", "\n", "We would rather classify the employees according to a range of experience as years as it is effective to from classes. Our classification is as follows:\n", "- New: Less than 3 years at a company\n", "- Experienced: 3-6 years at a company\n", "- Established: 7-10 years at a company\n", "- Veteran: 11 or more years at a company" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "def service_category(val):\n", " if pd.isna(val):\n", " return np.nan\n", " elif val < 3:\n", " return 'New'\n", " elif val < 7:\n", " return 'Experienced'\n", " elif val < 11:\n", " return 'Established'\n", " else:\n", " return 'Veteran'" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NaN 88\n", "Less than 1 year 73\n", "1-2 64\n", "3-4 63\n", "5-6 33\n", "11-20 26\n", "5.0 23\n", "1.0 22\n", "7-10 21\n", "-0.0 20\n", "3.0 20\n", "6.0 17\n", "4.0 16\n", "9.0 14\n", "2.0 14\n", "7.0 13\n", "More than 20 years 10\n", "13.0 8\n", "8.0 8\n", "15.0 7\n", "20.0 7\n", "17.0 6\n", "12.0 6\n", "10.0 6\n", "14.0 6\n", "22.0 6\n", "16.0 5\n", "18.0 5\n", "23.0 4\n", "24.0 4\n", "11.0 4\n", "19.0 3\n", "39.0 3\n", "21.0 3\n", "32.0 3\n", "26.0 2\n", "28.0 2\n", "30.0 2\n", "25.0 2\n", "36.0 2\n", "27.0 1\n", "29.0 1\n", "31.0 1\n", "33.0 1\n", "42.0 1\n", "34.0 1\n", "35.0 1\n", "49.0 1\n", "38.0 1\n", "41.0 1\n", "Name: institute_service, dtype: int64" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# combined['institute_service'].astype('str')\n", "combined['institute_service'].value_counts(dropna = False)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined['institute_service'] = combined['institute_service'].astype('str')\n", "type(combined['institute_service'][0])" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 7.0\n", "1 18.0\n", "2 3.0\n", "3 15.0\n", "4 3.0\n", " ... \n", "646 6\n", "647 2\n", "648 nan\n", "649 6\n", "650 4\n", "Name: institute_service, Length: 651, dtype: object" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# cleaning columns entries/ transforming them into the same format\n", "def clean(val):\n", " if pd.isna(val):\n", " return np.nan\n", " if '-' in val:\n", " return float(val[-1])\n", " else:\n", " res = [i for i in val.split()]\n", " return res[0]\n", " \n", "combined['institute_service'] = combined['institute_service'].apply(clean)\n", "combined['institute_service']" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " combined['institute_service'][combined['institute_service'] == 'Less'] = 1.0\n", ":2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " combined['institute_service'][combined['institute_service'] == 'More'] = 20.0\n" ] }, { "data": { "text/plain": [ "1.0 95\n", "NaN 88\n", "4.0 79\n", "2.0 78\n", "0.0 67\n", "6.0 50\n", "5.0 23\n", "3.0 20\n", "20.0 17\n", "9.0 14\n", "7.0 13\n", "13.0 8\n", "8.0 8\n", "15.0 7\n", "10.0 6\n", "12.0 6\n", "17.0 6\n", "22.0 6\n", "14.0 6\n", "16.0 5\n", "18.0 5\n", "11.0 4\n", "24.0 4\n", "23.0 4\n", "19.0 3\n", "21.0 3\n", "39.0 3\n", "32.0 3\n", "26.0 2\n", "30.0 2\n", "36.0 2\n", "28.0 2\n", "25.0 2\n", "29.0 1\n", "42.0 1\n", "35.0 1\n", "27.0 1\n", "41.0 1\n", "38.0 1\n", "49.0 1\n", "34.0 1\n", "33.0 1\n", "31.0 1\n", "Name: institute_service, dtype: int64" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined['institute_service'][combined['institute_service'] == 'Less'] = 1.0\n", "combined['institute_service'][combined['institute_service'] == 'More'] = 20.0\n", "combined['institute_service'] = combined['institute_service'].astype('float')\n", "combined['institute_service'].value_counts(dropna = False)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0 95\n", "NaN 88\n", "4.0 79\n", "2.0 78\n", "0.0 67\n", "6.0 50\n", "5.0 23\n", "3.0 20\n", "20.0 17\n", "9.0 14\n", "7.0 13\n", "13.0 8\n", "8.0 8\n", "15.0 7\n", "10.0 6\n", "12.0 6\n", "17.0 6\n", "22.0 6\n", "14.0 6\n", "16.0 5\n", "18.0 5\n", "11.0 4\n", "24.0 4\n", "23.0 4\n", "19.0 3\n", "21.0 3\n", "39.0 3\n", "32.0 3\n", "26.0 2\n", "30.0 2\n", "36.0 2\n", "28.0 2\n", "25.0 2\n", "29.0 1\n", "42.0 1\n", "35.0 1\n", "27.0 1\n", "41.0 1\n", "38.0 1\n", "49.0 1\n", "34.0 1\n", "33.0 1\n", "31.0 1\n", "Name: institute_service, dtype: int64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined['institute_service'] = combined['institute_service'].astype('float')\n", "combined['institute_service'].value_counts(dropna = False)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "New 240\n", "Experienced 172\n", "Veteran 110\n", "NaN 88\n", "Established 41\n", "Name: service_cat, dtype: int64" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined['service_cat'] = combined['institute_service'].apply(service_category)\n", "\n", "combined['service_cat'].value_counts(dropna=False)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 36-40\n", "1 41-45\n", "2 31-35\n", "3 46-50\n", "4 31-35\n", " ... \n", "646 21 25\n", "647 51-55\n", "648 NaN\n", "649 51-55\n", "650 26 30\n", "Name: age, Length: 651, dtype: object" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined['age'] " ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "def convert_range(val):\n", " if pd.notna(val):\n", " if ' ' in val:\n", " numbers = [int(i) for i in val.split() if i.isdigit()]\n", " else:\n", " numbers = [int(i) for i in val.split('-') if i.isdigit()]\n", " return int(sum(numbers) / len(numbers))\n", " return np.nan\n", "combined['age'] = combined['age'].apply(convert_range)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "43.0 93\n", "48.0 81\n", "38.0 73\n", "53.0 71\n", "28.0 67\n", "23.0 62\n", "33.0 61\n", "NaN 55\n", "56.0 29\n", "58.0 26\n", "61.0 23\n", "20.0 10\n", "Name: age, dtype: int64" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined['age'].value_counts(dropna = False)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Senior 230\n", "Middle 227\n", "Junior 139\n", "NaN 55\n", "Name: age_cat, dtype: int64" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def age_category(val):\n", " if pd.isna(val):\n", " return np.nan\n", " elif val < 31:\n", " return 'Junior'\n", " elif val < 46:\n", " return 'Middle'\n", " else:\n", " return 'Senior'\n", "\n", "combined['age'] = combined['age'].astype('float')\n", "\n", "combined['age_cat'] = combined['age'].apply(age_category)\n", "\n", "combined['age_cat'].value_counts(dropna=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Removing Irrelevant columns / Keeping Relevant Columns\n", "\n", "Since we have already used some columns to decide dissatisfaction, we no longer need those columns anymore. Hence, we will identify and keep only the columns we need for our analysis." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 651 entries, 0 to 650\n", "Data columns (total 49 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 id 651 non-null float64\n", " 1 separation_type 651 non-null object \n", " 2 cease_date 635 non-null float64\n", " 3 dete_start_date 283 non-null float64\n", " 4 role_start_date 271 non-null float64\n", " 5 position 598 non-null object \n", " 6 region 265 non-null object \n", " 7 employment_status 597 non-null object \n", " 8 career_move_to_public_sector 311 non-null object \n", " 9 career_move_to_private_sector 311 non-null object \n", " 10 interpersonal_conflicts 311 non-null object \n", " 11 job_dissatisfaction 311 non-null object \n", " 12 dissatisfaction_with_the_department 311 non-null object \n", " 13 physical_work_environment 311 non-null object \n", " 14 lack_of_recognition 311 non-null object \n", " 15 lack_of_job_security 311 non-null object \n", " 16 work_location 311 non-null object \n", " 17 employment_conditions 311 non-null object \n", " 18 maternity/family 311 non-null object \n", " 19 relocation 311 non-null object \n", " 20 study/travel 311 non-null object \n", " 21 ill_health 311 non-null object \n", " 22 traumatic_incident 311 non-null object \n", " 23 work_life_balance 311 non-null object \n", " 24 workload 311 non-null object \n", " 25 none_of_the_above 311 non-null object \n", " 26 gender 592 non-null object \n", " 27 age 596 non-null float64\n", " 28 separationtype 311 non-null object \n", " 29 institute_service 563 non-null float64\n", " 30 dissatisfied 643 non-null object \n", " 31 institute 651 non-null object \n", " 32 Institute 340 non-null object \n", " 33 WorkArea 340 non-null object \n", " 34 Contributing Factors. Career Move - Public Sector 332 non-null object \n", " 35 Contributing Factors. Career Move - Private Sector 332 non-null object \n", " 36 Contributing Factors. Career Move - Self-employment 332 non-null object \n", " 37 Contributing Factors. Ill Health 332 non-null object \n", " 38 Contributing Factors. Maternity/Family 332 non-null object \n", " 39 factors_diss 332 non-null object \n", " 40 factors_job_diss 332 non-null object \n", " 41 Contributing Factors. Interpersonal Conflict 332 non-null object \n", " 42 Contributing Factors. Study 332 non-null object \n", " 43 Contributing Factors. Travel 332 non-null object \n", " 44 Contributing Factors. Other 332 non-null object \n", " 45 Contributing Factors. NONE 332 non-null object \n", " 46 role_service 290 non-null object \n", " 47 service_cat 563 non-null object \n", " 48 age_cat 596 non-null object \n", "dtypes: float64(6), object(43)\n", "memory usage: 249.3+ KB\n" ] } ], "source": [ "combined.info()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcease_datepositionemployment_statusgenderageinstitute_servicedissatisfiedinstituteservice_catage_cat
672.130000e+022012.0TeacherPermanent Part-timeMale61.026.0TrueDETEVeteranSenior
2155.870000e+022010.0TeacherPermanent Part-timeFemale53.011.0FalseDETEVeteranSenior
1614.500000e+022012.0TeacherPermanent Full-timeMale38.0NaNTrueDETENaNMiddle
3576.342272e+172010.0Professional Officer (PO)Permanent Full-timeFemale38.00.0TrueTAFENewMiddle
2506.720000e+022013.0School Based Professional Staff (Therapist, nu...Temporary Full-timeFemale23.00.0FalseDETENewJunior
\n", "
" ], "text/plain": [ " id cease_date \\\n", "67 2.130000e+02 2012.0 \n", "215 5.870000e+02 2010.0 \n", "161 4.500000e+02 2012.0 \n", "357 6.342272e+17 2010.0 \n", "250 6.720000e+02 2013.0 \n", "\n", " position employment_status \\\n", "67 Teacher Permanent Part-time \n", "215 Teacher Permanent Part-time \n", "161 Teacher Permanent Full-time \n", "357 Professional Officer (PO) Permanent Full-time \n", "250 School Based Professional Staff (Therapist, nu... Temporary Full-time \n", "\n", " gender age institute_service dissatisfied institute service_cat \\\n", "67 Male 61.0 26.0 True DETE Veteran \n", "215 Female 53.0 11.0 False DETE Veteran \n", "161 Male 38.0 NaN True DETE NaN \n", "357 Female 38.0 0.0 True TAFE New \n", "250 Female 23.0 0.0 False DETE New \n", "\n", " age_cat \n", "67 Senior \n", "215 Senior \n", "161 Middle \n", "357 Middle \n", "250 Junior " ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# other columns are needed for our analysis. We our choosing the columns according to the question we are trying to\n", "# answer. We even dropped the bolean columns as we had used their information to create a new column which turns \n", "# out to be more precise\n", "combined = combined[['id', 'cease_date', 'position', 'employment_status', 'gender', 'age', \n", " 'institute_service', 'dissatisfied','institute','service_cat','age_cat']]\n", "combined.sample(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Handling missing values in combined\n" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0\n", "cease_date 16\n", "position 53\n", "employment_status 54\n", "gender 59\n", "age 55\n", "institute_service 88\n", "dissatisfied 8\n", "institute 0\n", "service_cat 88\n", "age_cat 55\n", "dtype: int64" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", " ... \n", "646 False\n", "647 False\n", "648 False\n", "649 False\n", "650 False\n", "Name: dissatisfied, Length: 651, dtype: bool" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index = combined['dissatisfied'].isna()\n", "index" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "322 NaN\n", "324 NaN\n", "345 NaN\n", "466 NaN\n", "472 NaN\n", "523 NaN\n", "543 NaN\n", "627 NaN\n", "Name: dissatisfied, dtype: object" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined.loc[index,'dissatisfied']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "About 1% values in dissatisfied is null so we can fill it with the majority value that is 'False'. " ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/saumyamundra/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:1765: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " isetter(loc, value)\n" ] } ], "source": [ "combined.loc[index,'dissatisfied'] = False" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 411\n", "True 240\n", "Name: dissatisfied, dtype: int64" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#combined['dissatisfied'] = combined['dissatisfied'].fillna(False)\n", "combined['dissatisfied'].value_counts(dropna = False)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0\n", "cease_date 16\n", "position 53\n", "employment_status 54\n", "gender 59\n", "age 55\n", "institute_service 88\n", "dissatisfied 0\n", "institute 0\n", "service_cat 88\n", "age_cat 55\n", "dtype: int64" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the columns age with its corresponding age_cat and institute_service with its corresponding service_cat, we observe a significant amount of missing values." ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcease_datepositionemployment_statusgenderageinstitute_servicedissatisfiedinstituteservice_catage_cat
1414.060000e+022012.0TeacherNaNNaNNaNNaNFalseDETENaNNaN
3018.040000e+022013.0Teacher AidePermanent Part-timeNaNNaNNaNFalseDETENaNNaN
3108.230000e+022013.0Teacher AideNaNNaNNaNNaNFalseDETENaNNaN
3116.341399e+172010.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
3226.341770e+172010.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
3246.341779e+172010.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
3256.341820e+172010.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
3266.341821e+172010.0NaNNaNNaNNaNNaNTrueTAFENaNNaN
3276.341831e+172010.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
3316.341934e+172010.0NaNNaNNaNNaNNaNTrueTAFENaNNaN
3356.342062e+172010.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
3366.342080e+172010.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
3376.342081e+172010.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
3456.342141e+172010.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
3476.342148e+172010.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
3486.342174e+172010.0NaNNaNNaNNaNNaNTrueTAFENaNNaN
3676.342574e+172010.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
3706.342661e+172010.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
3736.342679e+172011.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
3756.342686e+172011.0NaNNaNNaNNaNNaNTrueTAFENaNNaN
3786.342745e+172010.0NaNNaNNaNNaNNaNTrueTAFENaNNaN
3796.342746e+172010.0NaNNaNNaNNaNNaNTrueTAFENaNNaN
3856.342978e+17NaNNaNNaNNaNNaNNaNTrueTAFENaNNaN
3976.343264e+172011.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
4026.343283e+17NaNNaNNaNNaNNaNNaNTrueTAFENaNNaN
4056.343333e+172011.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
4196.343811e+172011.0NaNNaNNaNNaNNaNTrueTAFENaNNaN
4406.344568e+172010.0NaNNaNNaNNaNNaNTrueTAFENaNNaN
4536.344993e+172010.0NaNNaNNaNNaNNaNTrueTAFENaNNaN
4616.345234e+172011.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
4666.345510e+172011.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
4726.345581e+172011.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
4746.345632e+172011.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
4766.345647e+172011.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
4956.345925e+172011.0NaNNaNNaNNaNNaNTrueTAFENaNNaN
5136.346668e+172012.0NaNNaNNaNNaNNaNTrueTAFENaNNaN
5196.346832e+172012.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
5236.346963e+172012.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
5436.347827e+17NaNNaNNaNNaNNaNNaNFalseTAFENaNNaN
5546.348110e+172012.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
5566.348112e+172012.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
5586.348129e+172012.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
5626.348187e+172012.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
5816.348785e+172012.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
5966.349156e+172013.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
5996.349375e+172013.0NaNNaNNaNNaNNaNTrueTAFENaNNaN
6026.349384e+172013.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
6246.350055e+172013.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
6256.350055e+172013.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
6276.350124e+172013.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
6426.350496e+172013.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
6456.350652e+172013.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
6486.350677e+172013.0NaNNaNNaNNaNNaNFalseTAFENaNNaN
\n", "
" ], "text/plain": [ " id cease_date position employment_status gender age \\\n", "141 4.060000e+02 2012.0 Teacher NaN NaN NaN \n", "301 8.040000e+02 2013.0 Teacher Aide Permanent Part-time NaN NaN \n", "310 8.230000e+02 2013.0 Teacher Aide NaN NaN NaN \n", "311 6.341399e+17 2010.0 NaN NaN NaN NaN \n", "322 6.341770e+17 2010.0 NaN NaN NaN NaN \n", "324 6.341779e+17 2010.0 NaN NaN NaN NaN \n", "325 6.341820e+17 2010.0 NaN NaN NaN NaN \n", "326 6.341821e+17 2010.0 NaN NaN NaN NaN \n", "327 6.341831e+17 2010.0 NaN NaN NaN NaN \n", "331 6.341934e+17 2010.0 NaN NaN NaN NaN \n", "335 6.342062e+17 2010.0 NaN NaN NaN NaN \n", "336 6.342080e+17 2010.0 NaN NaN NaN NaN \n", "337 6.342081e+17 2010.0 NaN NaN NaN NaN \n", "345 6.342141e+17 2010.0 NaN NaN NaN NaN \n", "347 6.342148e+17 2010.0 NaN NaN NaN NaN \n", "348 6.342174e+17 2010.0 NaN NaN NaN NaN \n", "367 6.342574e+17 2010.0 NaN NaN NaN NaN \n", "370 6.342661e+17 2010.0 NaN NaN NaN NaN \n", "373 6.342679e+17 2011.0 NaN NaN NaN NaN \n", "375 6.342686e+17 2011.0 NaN NaN NaN NaN \n", "378 6.342745e+17 2010.0 NaN NaN NaN NaN \n", "379 6.342746e+17 2010.0 NaN NaN NaN NaN \n", "385 6.342978e+17 NaN NaN NaN NaN NaN \n", "397 6.343264e+17 2011.0 NaN NaN NaN NaN \n", "402 6.343283e+17 NaN NaN NaN NaN NaN \n", "405 6.343333e+17 2011.0 NaN NaN NaN NaN \n", "419 6.343811e+17 2011.0 NaN NaN NaN NaN \n", "440 6.344568e+17 2010.0 NaN NaN NaN NaN \n", "453 6.344993e+17 2010.0 NaN NaN NaN NaN \n", "461 6.345234e+17 2011.0 NaN NaN NaN NaN \n", "466 6.345510e+17 2011.0 NaN NaN NaN NaN \n", "472 6.345581e+17 2011.0 NaN NaN NaN NaN \n", "474 6.345632e+17 2011.0 NaN NaN NaN NaN \n", "476 6.345647e+17 2011.0 NaN NaN NaN NaN \n", "495 6.345925e+17 2011.0 NaN NaN NaN NaN \n", "513 6.346668e+17 2012.0 NaN NaN NaN NaN \n", "519 6.346832e+17 2012.0 NaN NaN NaN NaN \n", "523 6.346963e+17 2012.0 NaN NaN NaN NaN \n", "543 6.347827e+17 NaN NaN NaN NaN NaN \n", "554 6.348110e+17 2012.0 NaN NaN NaN NaN \n", "556 6.348112e+17 2012.0 NaN NaN NaN NaN \n", "558 6.348129e+17 2012.0 NaN NaN NaN NaN \n", "562 6.348187e+17 2012.0 NaN NaN NaN NaN \n", "581 6.348785e+17 2012.0 NaN NaN NaN NaN \n", "596 6.349156e+17 2013.0 NaN NaN NaN NaN \n", "599 6.349375e+17 2013.0 NaN NaN NaN NaN \n", "602 6.349384e+17 2013.0 NaN NaN NaN NaN \n", "624 6.350055e+17 2013.0 NaN NaN NaN NaN \n", "625 6.350055e+17 2013.0 NaN NaN NaN NaN \n", "627 6.350124e+17 2013.0 NaN NaN NaN NaN \n", "642 6.350496e+17 2013.0 NaN NaN NaN NaN \n", "645 6.350652e+17 2013.0 NaN NaN NaN NaN \n", "648 6.350677e+17 2013.0 NaN NaN NaN NaN \n", "\n", " institute_service dissatisfied institute service_cat age_cat \n", "141 NaN False DETE NaN NaN \n", "301 NaN False DETE NaN NaN \n", "310 NaN False DETE NaN NaN \n", "311 NaN False TAFE NaN NaN \n", "322 NaN False TAFE NaN NaN \n", "324 NaN False TAFE NaN NaN \n", "325 NaN False TAFE NaN NaN \n", "326 NaN True TAFE NaN NaN \n", "327 NaN False TAFE NaN NaN \n", "331 NaN True TAFE NaN NaN \n", "335 NaN False TAFE NaN NaN \n", "336 NaN False TAFE NaN NaN \n", "337 NaN False TAFE NaN NaN \n", "345 NaN False TAFE NaN NaN \n", "347 NaN False TAFE NaN NaN \n", "348 NaN True TAFE NaN NaN \n", "367 NaN False TAFE NaN NaN \n", "370 NaN False TAFE NaN NaN \n", "373 NaN False TAFE NaN NaN \n", "375 NaN True TAFE NaN NaN \n", "378 NaN True TAFE NaN NaN \n", "379 NaN True TAFE NaN NaN \n", "385 NaN True TAFE NaN NaN \n", "397 NaN False TAFE NaN NaN \n", "402 NaN True TAFE NaN NaN \n", "405 NaN False TAFE NaN NaN \n", "419 NaN True TAFE NaN NaN \n", "440 NaN True TAFE NaN NaN \n", "453 NaN True TAFE NaN NaN \n", "461 NaN False TAFE NaN NaN \n", "466 NaN False TAFE NaN NaN \n", "472 NaN False TAFE NaN NaN \n", "474 NaN False TAFE NaN NaN \n", "476 NaN False TAFE NaN NaN \n", "495 NaN True TAFE NaN NaN \n", "513 NaN True TAFE NaN NaN \n", "519 NaN False TAFE NaN NaN \n", "523 NaN False TAFE NaN NaN \n", "543 NaN False TAFE NaN NaN \n", "554 NaN False TAFE NaN NaN \n", "556 NaN False TAFE NaN NaN \n", "558 NaN False TAFE NaN NaN \n", "562 NaN False TAFE NaN NaN \n", "581 NaN False TAFE NaN NaN \n", "596 NaN False TAFE NaN NaN \n", "599 NaN True TAFE NaN NaN \n", "602 NaN False TAFE NaN NaN \n", "624 NaN False TAFE NaN NaN \n", "625 NaN False TAFE NaN NaN \n", "627 NaN False TAFE NaN NaN \n", "642 NaN False TAFE NaN NaN \n", "645 NaN False TAFE NaN NaN \n", "648 NaN False TAFE NaN NaN " ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# checking where the columns intersect\n", "age_service_null_bool = (combined['service_cat'].isnull()) & (combined['age_cat'].isnull())\n", "age_service_null = combined[age_service_null_bool]\n", "age_service_null" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are all null correspondingly so even if we want to fill in the missing values, we cant as the emloyee's data is missing in all columns." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/saumyamundra/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py:4163: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " return super().drop(\n" ] } ], "source": [ "combined.drop(combined[age_service_null_bool].index, inplace=True)\n", "combined.reset_index(drop=True, inplace=True)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcease_datepositionemployment_statusgenderageinstitute_servicedissatisfiedinstituteservice_catage_cat
04.000000e+002012.0TeacherPermanent Full-timeFemale38.07.0FalseDETEEstablishedMiddle
16.000000e+002012.0Guidance OfficerPermanent Full-timeFemale43.018.0TrueDETEVeteranMiddle
29.000000e+002012.0TeacherPermanent Full-timeFemale33.03.0FalseDETEExperiencedMiddle
31.000000e+012012.0Teacher AidePermanent Part-timeFemale48.015.0TrueDETEVeteranSenior
41.200000e+012012.0TeacherPermanent Full-timeMale33.03.0FalseDETEExperiencedMiddle
....................................
5936.350599e+172013.0Administration (AO)Temporary Full-timeFemale28.02.0FalseTAFENewJunior
5946.350660e+172013.0Operational (OO)Temporary Full-timeMale23.06.0FalseTAFEExperiencedJunior
5956.350668e+172013.0Teacher (including LVT)Temporary Full-timeMale53.02.0FalseTAFENewSenior
5966.350704e+172013.0Teacher (including LVT)Permanent Full-timeFemale53.06.0FalseTAFEExperiencedSenior
5976.350730e+172013.0Administration (AO)Contract/casualFemale28.04.0FalseTAFEExperiencedJunior
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598 rows × 11 columns

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" ], "text/plain": [ " id cease_date position employment_status \\\n", "0 4.000000e+00 2012.0 Teacher Permanent Full-time \n", "1 6.000000e+00 2012.0 Guidance Officer Permanent Full-time \n", "2 9.000000e+00 2012.0 Teacher Permanent Full-time \n", "3 1.000000e+01 2012.0 Teacher Aide Permanent Part-time \n", "4 1.200000e+01 2012.0 Teacher Permanent Full-time \n", ".. ... ... ... ... \n", "593 6.350599e+17 2013.0 Administration (AO) Temporary Full-time \n", "594 6.350660e+17 2013.0 Operational (OO) Temporary Full-time \n", "595 6.350668e+17 2013.0 Teacher (including LVT) Temporary Full-time \n", "596 6.350704e+17 2013.0 Teacher (including LVT) Permanent Full-time \n", "597 6.350730e+17 2013.0 Administration (AO) Contract/casual \n", "\n", " gender age institute_service dissatisfied institute service_cat \\\n", "0 Female 38.0 7.0 False DETE Established \n", "1 Female 43.0 18.0 True DETE Veteran \n", "2 Female 33.0 3.0 False DETE Experienced \n", "3 Female 48.0 15.0 True DETE Veteran \n", "4 Male 33.0 3.0 False DETE Experienced \n", ".. ... ... ... ... ... ... \n", "593 Female 28.0 2.0 False TAFE New \n", "594 Male 23.0 6.0 False TAFE Experienced \n", "595 Male 53.0 2.0 False TAFE New \n", "596 Female 53.0 6.0 False TAFE Experienced \n", "597 Female 28.0 4.0 False TAFE Experienced \n", "\n", " age_cat \n", "0 Middle \n", "1 Middle \n", "2 Middle \n", "3 Senior \n", "4 Middle \n", ".. ... \n", "593 Junior \n", "594 Junior \n", "595 Senior \n", "596 Senior \n", "597 Junior \n", "\n", "[598 rows x 11 columns]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have now eliminated correspponding null values of age_cat and service_cat. Since our question is dependent on these columns, we will try to fill the missing values. Our next step would be to fill both those columns using information from each other. " ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcease_datepositionemployment_statusgenderageinstitute_servicedissatisfiedinstituteservice_catage_cat
717.02012.0Teacher AidePermanent Part-timeMale61.0NaNTrueDETENaNSenior
1740.02012.0TeacherPermanent Full-timeFemale23.0NaNTrueDETENaNJunior
37107.02012.0Teacher AideTemporary Part-timeFemale48.0NaNTrueDETENaNSenior
50141.02012.0Teacher AidePermanent Part-timeFemale53.0NaNFalseDETENaNSenior
62197.02012.0Teacher AidePermanent Part-timeFemale48.0NaNFalseDETENaNSenior
95289.02013.0Public ServantPermanent Full-timeFemale28.0NaNTrueDETENaNJunior
96292.02013.0Teacher AidePermanent Part-timeFemale58.0NaNFalseDETENaNSenior
97294.02012.0Schools OfficerPermanent Part-timeNaN61.0NaNFalseDETENaNSenior
101302.02012.0School Administrative StaffPermanent Part-timeFemale48.0NaNFalseDETENaNSenior
117344.02012.0School Administrative StaffPermanent Part-timeFemale43.0NaNFalseDETENaNMiddle
124366.02012.0TeacherPermanent Full-timeMale28.0NaNFalseDETENaNJunior
132380.02012.0TeacherPermanent Part-timeFemale28.0NaNFalseDETENaNJunior
140400.02013.0CleanerNaNMale58.0NaNTrueDETENaNSenior
142408.02013.0Technical OfficerTemporary Part-timeMale61.0NaNFalseDETENaNSenior
144410.02012.0Teacher AidePermanent Part-timeFemale38.0NaNFalseDETENaNMiddle
157439.02013.0TeacherPermanent Full-timeFemale23.0NaNFalseDETENaNJunior
160450.02012.0TeacherPermanent Full-timeMale38.0NaNTrueDETENaNMiddle
170472.02013.0TeacherPermanent Part-timeFemale58.0NaNTrueDETENaNSenior
179490.02012.0CleanerNaNFemale48.0NaNFalseDETENaNSenior
191532.02013.0CleanerPermanent Part-timeFemale58.0NaNTrueDETENaNSenior
195539.02013.0TeacherPermanent Full-timeMale28.0NaNTrueDETENaNJunior
244663.02013.0TeacherPermanent Part-timeFemale48.0NaNFalseDETENaNSenior
254685.0NaNTeacherPermanent Full-timeMale23.0NaNTrueDETENaNJunior
262696.0NaNTeacher AideCasualFemale48.0NaNFalseDETENaNSenior
268706.0NaNTeacher AidePermanent Full-timeFemale43.0NaNFalseDETENaNMiddle
269711.0NaNTeacherPermanent Full-timeFemale53.0NaNTrueDETENaNSenior
270714.02013.0Teacher AidePermanent Part-timeFemale61.0NaNFalseDETENaNSenior
272726.0NaNTeacherPermanent Full-timeFemale48.0NaNFalseDETENaNSenior
288772.0NaNCleanerPermanent Part-timeFemale61.0NaNFalseDETENaNSenior
290776.0NaNTeacher AidePermanent Part-timeFemale43.0NaNFalseDETENaNMiddle
293790.0NaNTeacherPermanent Full-timeFemale43.0NaNFalseDETENaNMiddle
296793.0NaNPublic ServantPermanent Part-timeFemale48.0NaNTrueDETENaNSenior
297796.02013.0CleanerPermanent Part-timeFemale38.0NaNFalseDETENaNMiddle
298799.0NaNPublic ServantPermanent Part-timeFemale38.0NaNFalseDETENaNMiddle
299800.0NaNTeacher AidePermanent Part-timeFemale38.0NaNFalseDETENaNMiddle
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" ], "text/plain": [ " id cease_date position employment_status \\\n", "7 17.0 2012.0 Teacher Aide Permanent Part-time \n", "17 40.0 2012.0 Teacher Permanent Full-time \n", "37 107.0 2012.0 Teacher Aide Temporary Part-time \n", "50 141.0 2012.0 Teacher Aide Permanent Part-time \n", "62 197.0 2012.0 Teacher Aide Permanent Part-time \n", "95 289.0 2013.0 Public Servant Permanent Full-time \n", "96 292.0 2013.0 Teacher Aide Permanent Part-time \n", "97 294.0 2012.0 Schools Officer Permanent Part-time \n", "101 302.0 2012.0 School Administrative Staff Permanent Part-time \n", "117 344.0 2012.0 School Administrative Staff Permanent Part-time \n", "124 366.0 2012.0 Teacher Permanent Full-time \n", "132 380.0 2012.0 Teacher Permanent Part-time \n", "140 400.0 2013.0 Cleaner NaN \n", "142 408.0 2013.0 Technical Officer Temporary Part-time \n", "144 410.0 2012.0 Teacher Aide Permanent Part-time \n", "157 439.0 2013.0 Teacher Permanent Full-time \n", "160 450.0 2012.0 Teacher Permanent Full-time \n", "170 472.0 2013.0 Teacher Permanent Part-time \n", "179 490.0 2012.0 Cleaner NaN \n", "191 532.0 2013.0 Cleaner Permanent Part-time \n", "195 539.0 2013.0 Teacher Permanent Full-time \n", "244 663.0 2013.0 Teacher Permanent Part-time \n", "254 685.0 NaN Teacher Permanent Full-time \n", "262 696.0 NaN Teacher Aide Casual \n", "268 706.0 NaN Teacher Aide Permanent Full-time \n", "269 711.0 NaN Teacher Permanent Full-time \n", "270 714.0 2013.0 Teacher Aide Permanent Part-time \n", "272 726.0 NaN Teacher Permanent Full-time \n", "288 772.0 NaN Cleaner Permanent Part-time \n", "290 776.0 NaN Teacher Aide Permanent Part-time \n", "293 790.0 NaN Teacher Permanent Full-time \n", "296 793.0 NaN Public Servant Permanent Part-time \n", "297 796.0 2013.0 Cleaner Permanent Part-time \n", "298 799.0 NaN Public Servant Permanent Part-time \n", "299 800.0 NaN Teacher Aide Permanent Part-time \n", "\n", " gender age institute_service dissatisfied institute service_cat \\\n", "7 Male 61.0 NaN True DETE NaN \n", "17 Female 23.0 NaN True DETE NaN \n", "37 Female 48.0 NaN True DETE NaN \n", "50 Female 53.0 NaN False DETE NaN \n", "62 Female 48.0 NaN False DETE NaN \n", "95 Female 28.0 NaN True DETE NaN \n", "96 Female 58.0 NaN False DETE NaN \n", "97 NaN 61.0 NaN False DETE NaN \n", "101 Female 48.0 NaN False DETE NaN \n", "117 Female 43.0 NaN False DETE NaN \n", "124 Male 28.0 NaN False DETE NaN \n", "132 Female 28.0 NaN False DETE NaN \n", "140 Male 58.0 NaN True DETE NaN \n", "142 Male 61.0 NaN False DETE NaN \n", "144 Female 38.0 NaN False DETE NaN \n", "157 Female 23.0 NaN False DETE NaN \n", "160 Male 38.0 NaN True DETE NaN \n", "170 Female 58.0 NaN True DETE NaN \n", "179 Female 48.0 NaN False DETE NaN \n", "191 Female 58.0 NaN True DETE NaN \n", "195 Male 28.0 NaN True DETE NaN \n", "244 Female 48.0 NaN False DETE NaN \n", "254 Male 23.0 NaN True DETE NaN \n", "262 Female 48.0 NaN False DETE NaN \n", "268 Female 43.0 NaN False DETE NaN \n", "269 Female 53.0 NaN True DETE NaN \n", "270 Female 61.0 NaN False DETE NaN \n", "272 Female 48.0 NaN False DETE NaN \n", "288 Female 61.0 NaN False DETE NaN \n", "290 Female 43.0 NaN False DETE NaN \n", "293 Female 43.0 NaN False DETE NaN \n", "296 Female 48.0 NaN True DETE NaN \n", "297 Female 38.0 NaN False DETE NaN \n", "298 Female 38.0 NaN False DETE NaN \n", "299 Female 38.0 NaN False DETE NaN \n", "\n", " age_cat \n", "7 Senior \n", "17 Junior \n", "37 Senior \n", "50 Senior \n", "62 Senior \n", "95 Junior \n", "96 Senior \n", "97 Senior \n", "101 Senior \n", "117 Middle \n", "124 Junior \n", "132 Junior \n", "140 Senior \n", "142 Senior \n", "144 Middle \n", "157 Junior \n", "160 Middle \n", "170 Senior \n", "179 Senior \n", "191 Senior \n", "195 Junior \n", "244 Senior \n", "254 Junior \n", "262 Senior \n", "268 Middle \n", "269 Senior \n", "270 Senior \n", "272 Senior \n", "288 Senior \n", "290 Middle \n", "293 Middle \n", "296 Senior \n", "297 Middle \n", "298 Middle \n", "299 Middle " ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "service_null = combined[combined['service_cat'].isnull()]\n", "service_null" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To fill in values in service_cat, we will fill in the most frequent groups that the corresponding age group represents.\n", "Our boolean indexing will also state as institute to be 'DETE' because even thought the data is combined, corresponding values for age and service category depend on the separate institution." ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Junior :\n", "Experienced 27\n", "New 23\n", "Established 8\n", "Name: service_cat, dtype: int64\n", "Middle :\n", "Experienced 33\n", "Veteran 32\n", "New 24\n", "Established 20\n", "Name: service_cat, dtype: int64\n", "Senior :\n", "Veteran 67\n", "Experienced 16\n", "Established 13\n", "New 8\n", "Name: service_cat, dtype: int64\n" ] } ], "source": [ "age_cat_list = ['Junior','Middle','Senior']\n", "\n", "\n", "for i in age_cat_list:\n", " print(i,\":\")\n", " print(combined[(combined['institute'] == 'DETE')&(combined['age_cat'] == i)]['service_cat'].value_counts())" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['DETE'], dtype=object)" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "service_null['institute'].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since only DETE is the institute for which service_cat, we dont have to search for corresponding age groups for TAFE.\n", "\n", "From above, we can say that:\n", "- We will fill in the values in the service_cat field, where the value of the age_cat field is Junior, with the 'Experienced'\n", "- We will fill in the values in the service_cat field, where the value of the age_cat field is Middle, with the 'Veteran'.\n", "- We will fill in the values in the service_cat field, where the value of the age_cat field is Senior, with the 'Veteran'." ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "max_values = ['Experienced', 'Established', 'Veteran']\n" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/saumyamundra/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:1765: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " isetter(loc, value)\n" ] } ], "source": [ "for i,j in zip(age_cat_list,max_values):\n", " sort_bool = (combined['institute'] == 'DETE') & (combined['age_cat'] == i) & (combined['institute_service'].isnull())\n", " combined.loc[sort_bool,'service_cat'] = j" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined['service_cat'].isnull().sum()" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcease_datepositionemployment_statusgenderageinstitute_servicedissatisfiedinstituteservice_catage_cat
68215.02012.0School Administrative StaffPermanent Part-timeFemaleNaN13.0FalseDETEVeteranNaN
93286.02012.0CleanerPermanent Full-timeFemaleNaN0.0FalseDETENewNaN
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" ], "text/plain": [ " id cease_date position employment_status \\\n", "68 215.0 2012.0 School Administrative Staff Permanent Part-time \n", "93 286.0 2012.0 Cleaner Permanent Full-time \n", "\n", " gender age institute_service dissatisfied institute service_cat age_cat \n", "68 Female NaN 13.0 False DETE Veteran NaN \n", "93 Female NaN 0.0 False DETE New NaN " ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined[combined['age_cat'].isnull()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can continue similarly by finding maximum frequency for the corresponding service category. Again we will only check in DETE." ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Veteran :\n", "Senior 86\n", "Middle 32\n", "NaN 1\n", "Name: age_cat, dtype: int64\n", "New :\n", "Middle 24\n", "Junior 23\n", "Senior 8\n", "NaN 1\n", "Name: age_cat, dtype: int64\n" ] } ], "source": [ "service_cat_list = ['Veteran', 'New']\n", "for i in service_cat_list:\n", " print(i,\":\")\n", " print(combined[(combined['institute'] == 'DETE')&(combined['service_cat'] == i)]['age_cat'].value_counts(dropna = False))" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/saumyamundra/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:1765: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " isetter(loc, value)\n" ] } ], "source": [ "max_values = ['Senior','Middle']\n", "for i,j in zip(service_cat_list,max_values):\n", " sort_bool = (combined['institute'] == 'DETE') & (combined['service_cat'] == i) & (combined['age'].isnull())\n", " combined.loc[sort_bool,'age_cat'] = j" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcease_datepositionemployment_statusgenderageinstitute_servicedissatisfiedinstituteservice_catage_cat
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [id, cease_date, position, employment_status, gender, age, institute_service, dissatisfied, institute, service_cat, age_cat]\n", "Index: []" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined[combined['age_cat'].isnull()]" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 598 entries, 0 to 597\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 id 598 non-null float64\n", " 1 cease_date 585 non-null float64\n", " 2 position 595 non-null object \n", " 3 employment_status 596 non-null object \n", " 4 gender 592 non-null object \n", " 5 age 596 non-null float64\n", " 6 institute_service 563 non-null float64\n", " 7 dissatisfied 598 non-null object \n", " 8 institute 598 non-null object \n", " 9 service_cat 598 non-null object \n", " 10 age_cat 598 non-null object \n", "dtypes: float64(4), object(7)\n", "memory usage: 51.5+ KB\n" ] } ], "source": [ "combined.info()" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0\n", "cease_date 13\n", "position 3\n", "employment_status 2\n", "gender 6\n", "age 2\n", "institute_service 35\n", "dissatisfied 0\n", "institute 0\n", "service_cat 0\n", "age_cat 0\n", "dtype: int64" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are some missing values in columns such as cease_date,position,employment_status,gender,age and institute_service. But these values are not as important as dissatisfied,service_cat and age_cat, and since they have 0 null values, we can move ahead with our analysis.\n", "\n", "We may debate that we may need to handle the unll values of institute_service. we really do not need to as we have used it to classify other categories." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Analysis\n", "\n", "Now we'll look at the number of dissatisfied employees who left the institute at different stages of their career. We'll sort the values in descending order and then visualize the results." ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False 372\n", "True 226\n", "Name: dissatisfied, dtype: int64" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined['dissatisfied'].value_counts()" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dissatisfiedDETETAFE
service_cat
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Veteran68635.0
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Established2626NaN
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" ], "text/plain": [ " dissatisfied DETE TAFE\n", "service_cat \n", "New 69 21 48.0\n", "Veteran 68 63 5.0\n", "Experienced 63 39 24.0\n", "Established 26 26 NaN" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diss_sc = combined.pivot_table(index='service_cat', values='dissatisfied', aggfunc=np.sum)['dissatisfied'].sort_values(ascending=False).to_frame()\n", "\n", "DETE_sc = combined[combined['institute'] == 'DETE'].reset_index(drop=True)\n", "diss_sc['DETE'] = DETE_sc.pivot_table(index='service_cat', values='dissatisfied', aggfunc=np.sum)['dissatisfied'].sort_values(ascending=False)\n", "\n", "TAFE_sc = combined[combined['institute'] == 'TAFE'].reset_index(drop=True)\n", "diss_sc['TAFE'] = TAFE_sc.pivot_table(index='service_cat', values='dissatisfied', aggfunc=np.sum)['dissatisfied'].sort_values(ascending=False)\n", "\n", "diss_sc" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "diss_sc = diss_sc.rename(columns = {'dissatisfied':'TOTAL'})" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['New', 'Veteran', 'Experienced', 'Established'], dtype='object', name='service_cat')" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diss_sc.index" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "# can change the name attribute\n", "diss_sc.index.name = 'Service Category'" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TOTALDETETAFE
Service Category
New692148.0
Veteran68635.0
Experienced633924.0
Established2626NaN
\n", "
" ], "text/plain": [ " TOTAL DETE TAFE\n", "Service Category \n", "New 69 21 48.0\n", "Veteran 68 63 5.0\n", "Experienced 63 39 24.0\n", "Established 26 26 NaN" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diss_sc" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TOTALDETETAFE
Service Category
New692148
Veteran68635
Experienced633924
Established26260
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" ], "text/plain": [ " TOTAL DETE TAFE\n", "Service Category \n", "New 69 21 48\n", "Veteran 68 63 5\n", "Experienced 63 39 24\n", "Established 26 26 0" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diss_sc = diss_sc.fillna(0)\n", "diss_sc['TAFE'] = diss_sc['TAFE'].astype('int')\n", "diss_sc" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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uQyum+SjpRDOLlBkEzTzQBWyTd9J/5B12Dunq+FLyg2JFdrxq8y4b14/U/v9R0vMNc/KOfiup5cKyFfGXzwfAyyx4Yvg4OuaJ4TdIRTG3Ax9vYfpv5+mrNnFtYdr1cpr/krfn23kfm0wqM/58jelE6ruq9GTu+3k73Je3YXm786FFtwfpKndujl/ziWeaf2L4QFJLldn5f3mRdFLeqkrc3UmNH2bkdXiVlLn/AtigldtyKVJfXn8m1ZG9n//DJ0nHTeUzHcsAv8zbvfTk/u9Ira8mUKU+j/TcxPi8bqXjZnh71in/j4+z4HzwB9ITw09RUb6f4/cg3bE+Qsrc3iEdM9+imSeGC27DM3P801q7L5d/lGdm1i3k3lhHkirrx3dycmwJIGl50p38ExGxXUvxO3C5E0h1C+tHxLNtnU+3qRMwy2W5+5GK3+7s5ORYFyNp5coKfkm9SMVTfUhdWjQqLVuT6r9ua08GAEtoL6Jm5SQdQCrK2Y9UP/Hj8C2wtd5ewM8k/R+pOKo/6Up8PVKdwDn1ToCkb5CecziUVMR1Unvn6UzAuoNRpIN1CvCdiLi+k9NjXdODpHqcHUh1EZDqUH4JnBoVXWLXyfGkhhkvAAdFxEPtnaHrBMzMurEudycwcODAGDp0aGcnw8ysS3n00Uf/ExErV4Z3uUxg6NChPPLII52dDDOzLkXSS9XC3TrIzKwbcyZgZtaNORMwM+vGGlInIGl9Uh8aJWuTXrZ+eQ4fSnq0fd+IeL0RaTKzJccHH3zA1KlTeffddzs7KZ2uT58+DB48mN69i3Vc25BMICKeAbYAyL0Avkx6uu4EYHxEnCLphPz7+EakycyWHFOnTqVfv34MHTqUGm8K6xYiglmzZjF16lTWWmutQtN0RnHQCOD5iHiJ1A3umBw+hvb1c29m3dS7777LgAEDunUGACCJAQMGtOqOqDMygf1I3cJC6vp2GkD+XqXaBJJGSXpE0iMzZ85sUDLNrCvp7hlASWu3Q0Mzgfwy7C8A17Zmuoi4MCKGRcSwlVde5FkHMzNro0Y/LPZZ0ns9p+ff0yUNiohp+U1GMxqcHjNbAg094dYOnV/TKZ9rdvysWbMYMWIEAK+++io9e/akdMF68803c9RRRzFx4kTmzZvH7rvvzmmnncZdd93F8cenKtDnnnuO1Vdfnb59+7LZZptx+eWXc8MNN/ClL32JSZMmscEGG6R0NDWx++6789RTT3XYujU6E9ifBUVBADeT+nY/JX/f1OD0AG3fYVraMcysexgwYABPPPEEAKNHj2a55Zbju9/9LhHBNttswze+8Q1uuukm5s6dy6hRo/jhD3/Iaaedxq677grA8OHD+c1vfsOwYcPmz3Ps2LF86lOfYty4cYwePbpuaW9YcVB+r+YupDcwlZwC7CLp2TzulGrTmpl1RXfeeSd9+vTh0EMPBaBnz56ceeaZXHLJJbzzTu1Xfb/11lvcf//9XHzxxYwbN66uaWxYJhAR70TEgIiYXRY2KyJGRMS6+fu1RqXHzKze/vWvf/Gxj31sobDll1+eIUOG8Nxzz9Wc7sYbb2S33XZjvfXWo3///jz22GN1S2OX60DOFn8uXjNLIqJqa51a4SVjx47lmGOOAWC//fZj7NixbLXVVnVJY4uZQH64azywa0S8V5dUmJktgTbeeGOuv37hdxi98cYbTJkyhXXWWafqNLNmzeLOO+/kqaeeQhJz585FEr/+9a/rksYWi4MiYi6wVpG4Zma2wIgRI3jnnXe4/PLLAZg7dy7HHXcchxxyCMsss0zVaa677joOPvhgXnrpJZqampgyZQprrbUW9913X13SWLQ46KfA+ZJOAqYC819HFhHz6pEwM7O2WlyKFiVxww038M1vfpOf//znzJs3j//5n//hV7/6Vc1pxo4dywknnLBQ2F577cVVV13F8ccfzzPPPMPgwYPnjzvzzDPZZ5992pzGopnARfn7oLIwkTKDnm1eupnZEqayOecaa6zBn/70p2anmTBhQtXhkqOPPnr+8AcffNCe5C2iaCZQrCciMzPrUgplArmzNyT1oKy/HzMz69oKVfZKWlHSVcC7wHM57AuSflHPxJmZWX0VbfFzATAbWBN4P4f9HfhyPRJlZmaNUbROYASwWkR8ICkAImKmpKpdP5uZWddQ9E5gNjCwPEDSEMB1A2ZmXVhrmoheL+mHQA9J2wG/IhUTmZktXkav0MHzm91ilJ49e7LpppvywQcf0KtXL0aOHMkxxxxDjx49mDBhAnvsscdCr3w88cQTOfnkk4FFu59+6KGH6Nu3L5tuuun8+Pvtt98izw90hKKZwKmkSuHfAb2BS4DfA2d1eIrMzLqgvn37zu9OesaMGRxwwAHMnj2bn/70pwBsv/323HLLLQtN8+Uvp2rV8u6nq82vngoVB0Xy24jYKCKWjYgN8+9oeWozs+5llVVW4cILL+Tcc89lcT9NFu5FVNJOpJfCrAa8AoyLiPH1SpiZWVe29tprM2/ePGbMSC9MvPfee9liiy3mj7/++utrdiIHMGfOnIXin3jiifPvHDpSoUxA0rHACcClwOPAEOAqSb+OiNM7PFXWPbWlHLdAWW1X5m65u7byu4BqxUHNaVRxUNE7geOAnSJi/ostJV0B3AE4EzAzq/DCCy/Qs2dPVlllFSZNmtTZyampNd1DV74G5wXKehM1M7Nk5syZHHHEERx55JHNvjxmcVDzTiD3E1QyGrhY0mhSV9JrAD8GTqpn4szM2qQTiglLZfilJqIHHXQQxx577PzxlXUCP/rRj9h7771bnF/JbrvtximndPxr2JsrDvqQBVf6paxs/4qwA1jQzbSZWbc1d+7cmuOGDx/O7Nm1M6bK7qdbml9Hai4TcPfRZmZLuJqZQKn76I4iaUXSXcMmpLuJw4BngKuBoUATsG9EvN6RyzUzs9qKNhFdATga2BJYrnxcRHym4LLOAv4aEXtLWgpYBvgBMD4iTpF0AqkZ6vFFE29mZu1TtInotaTXSN4AzGntQiQtD+wAHAIQEe8D70vaAxieo40BJuBMwMysYYpmAtsCAyKirS+3XBuYCVwqaXPgUeDblL2lLCKm1eqaWtIoYBTAkCFD2pgEMzOrVPQ5gfuADduxnF7AVsD5EbEl8Dap6KeQiLgwIoZFxLBSL3tmZtZ+Re8EDgH+LOlBYHr5iIj4WYHppwJTI+LB/Ps6UiYwXdKgfBcwCJhRMD1mZjVtOmbTliO1wpMjn2x2/KxZsxgxYgSwaLfQt912G4MHD+bcc8/l61//+vxphg4dSr9+/ejZsycA5513Hqutthobbrgh66+//vx4xx57LAcffHCHrk+5opnAL0kPiDUBy5eFF3piOCJelTRF0voR8QzpTWUT82ckcEr+vqlgeszMFhsDBgyY389PZbfQ5513Httuuy1jx45dKBMAuOuuuxg4cMH7upqamlhnnXUa0mdQSdFMYD9gvVL5fRsdBVyZWwa9ABxKKo66RtLhwGRgn3bM38xssTN27FhOP/10DjjgAF5++WVWX331zk7SQopmAi8Aba0UBiAingCGVRk1oj3zNTNbXE2ZMoVXX32Vrbfemn333Zerr756oa4kdtxxR3r27MnSSy/Ngw+m0vLnn39+oe4izjnnHLbffvu6pbFoJnAFcLOkc1i0TuDODk+VmdkSYNy4cey7775Aej3k4YcfvlAmUFkcBCy2xUHfyt+/qggPUvNPMzOrMHbsWKZPn86VV14JwCuvvMKzzz7Luuuu28kpW6BQJhAR7kfIzKwVnnnmGd5++21efvnl+WEnnXQS48aN48c//nEnpmxhhV8vaWbWVbTUpLMRxo4dyxe/+MWFwvbaay/222+/ZjOByjqBww47jKOPPrpeySzcd9AUajQHjQg/wmtmllXrFrpks802Y+LEiUBqDlpp6NChzJnT6p552qXoncBXKn4PInX7MK5jk2NmZo1UtE7g7sowSROAv5J6BzUzsy6oNe8YrvQefvGMmS0mIvzKc2j9dihaJ1DZP9AywP8Af2nV0szM6qBPnz7MmjWLAQMGLPYvdq+niGDWrFn06dOn8DRF6wTWqPj9NnAG6SEyM7NONXjwYKZOncrMmTM7Oymdrk+fPgwePLhw/KJ1Aoe2OUVmZnXWu3dv1lrLpdNt0WwmIGmHlmYQEfd0XHLMzKyRWroTuLJGeAArAsuSXjtpZmZdULOZQERU1gUgaVXSC+IPBS6oU7rMzKwBCjcRlbSipJOBZ4B+wGYR8a0WJjMzs8VYi5mApOUk/QR4kdRj6DYRcVhENNU7cWZmVl/NZgKSjiO9UOZjwKcj4sv59ZBmZrYEaKli+DTgNaA/cG61hzAiosUWRGZmtnhqKRPw8wFmZkuwlloHjWlUQszMrPHa04GcmZl1cQ17s5ikJuBNYC7wYUQMk9QfuBoYCjQB+0bE641Kk5lZd9foO4EdI2KLiBiWf58AjI+IdYHx+beZmTVIZxcH7QGU6h3GAHt2XlLMzLqfQpmApGMlbZGHt5U0WdILkrZrxbICuF3So5JG5bBVI2IaQP5epcbyR0l6RNIj7irWzKzjFK0T+A5wcR4+mfQugTeB3wLbFJzHJyPiFUmrAHdIerpoIiPiQuBCgGHDhvn1QWZmHaRoJrBCRMyW1A/YHNg5IuZKOr3ogiLilfw9Q9INwNbAdEmDImKapEHAjNaugJmZtV3ROoEpkj4B7AfckzOA5UktfVokadmcgSBpWeAzwFPAzcDIHG0kcFNrEm9mZu1T9E7ge8B1wPvAXjlsd+ChgtOvCtyQu53oBVwVEX+V9DBwjaTDgcnAPkUTbmZm7Vf09ZJ/BlarCL42f4pM/wKpGKkyfBYwosg8zMys4xV+WEzShsDepBY9RwLrAEsB/6xT2pZYm47ZtE3TPTnyyQ5OiZl1d0WbiO4D3AOsDhycg5cjtRIyM7MuqmjF8M+AXSLiCBZUBv+DKkU8ZmbWdRTNBFYhnfQhPfRV+nabfTOzLqxoJvAocFBF2H4Ubx1kZmaLoaIVw0eTunw4HFhW0m3AeqT2/mZm1kUVbSL6tKQNSM8G3AJMAW6JiLfqmTgzM6uvwk1EI+IdSX8HJkfEA3VMk5mZNUjRJqJDJN0PPA38Xw7bW9JF9UycmZnVV9GK4d8DtwL9gA9y2B3ALvVIlJmZNUbR4qCtgc9FxDxJAZB7FV2hfkkzM7N6K3onMB34aHmApI1Inb6ZmVkXVTQT+A1wi6RDgV6S9ie9IP7UuqXMzMzqrmgT0UskvQaMIjUPPRj4cUTcWMe0mZlZnRXKBCT1zCf8G+uaGjMza6iixUHTJJ0l6WN1TY2ZmTVU0Uzgs6TeQ2+RNEnSDyQNqWO6zMysAQplAhHxaEQcS3qfwHeAjYAnJd0l6bD83mAzM+tiit4JABAR80hPDT8NzCRlCgeSXkRf2cuomZkt5op2G7GSpK9Luo/UrfTqwMERsV5EjAB2Bc6uYzrNzKwOij4xPBW4i3Sivyki3isfGREPS7qpoxNnZmb1VTQTWDsipjcXISIOaWkmknoCjwAvR8TukvqTHjobCjQB+0bE6wXTZGZm7VS0Yni6pB0lXSLptvy9UxuW921gUtnvE4DxEbEuMD7/NjOzBilaJ/BV0hX7q8AfgWnAVZK+VnRBkgYDnwPKu5/eAxiTh8cAexadn5mZtV/R4qDvA7tEROll80i6Grge+N+C8/htnk+/srBVI2IaQERMk7RKtQkljSJ1WcGQIX48wcysoxRtIjoAmFgR9gzQv8jEknYHZkTEo61I23wRcWFEDIuIYSuvvHJbZmFmZlUUzQTuA86QtAxAfjjsNOBvBaf/JPAFSU3AOGAnSX8ApksalOc5CJjRirSbmVk7FS0OOoJ08p6dexPtT8oA9i8ycUScCJwIIGk48N2I+Iqk04CRwCn5281MzTrC6Da+72n07I5Nhy32inYlPQ34tKQ1gEHAKxExtQOWfwpwjaTDSS+o2acD5mlmZgXVzAQkVSsqejl/5o/PXUkUFhETgAl5eBYwojXTm5lZx2nuTuBDIJoZrzy+Z4emyMzMGqa5TGCthqXCzMw6Rc1MICJeqgyTJGAg8J+IaO4uwczMuoCiTwyvKOkK4F1gOjBH0hW57x8zM+uiij4ncCnQF9gCWA7YElgauKQ+yTIzs0Yo+pzAjsCgiJiTf0+SdAjwSl1SZWZmDVH0TuAZUnfP5YbkcDMz66KK3gmMB27P9QJTgDWArwBXSDqsFCkiXDxkZtaFFM0EtgOey9/b5bDngU/kD6RnBpwJmJl1IUW7jdix3gkxM7PGK3onAICk5Umtg+aLCFcOm5l1UYUyAUm7AL9n0cphdxthZtaFFW0ddBHwK2B5oHfZZ6k6pcvMzBqgaHFQH+DSiJhbz8SYmVljFb0TOBP4fu47yMzMlhBF7wSuB24DTpT0n/IREbF2h6fKzMwaomgmcB1wL3AtMKeFuGZm1kUUzQTWArZs7VvEzMxs8Va0TuAmYKd6JsTMzBqv6J3A0sDNku4lvU9gvog4uMNTZWZmDVE0E/hX/piZ2RKkaN9BP23PQiT1Ae4h3VH0Aq6LiJPym8muJj2J3ATsGxGvt2dZZmZWXLN1ApK+W/F7l4rfZxRcznvAThGxOentZLtJ2hY4ARgfEeuSuqs+oeD8zMysA7RUMfyTit9XV/z+apGFRPJW/lnqciKAPYAxOXwMsGeR+ZmZWcdoKROofEK4pd+1ZyT1lPQEMAO4IyIeBFaNiGkA+XuVGtOOkvSIpEdmzpxZdJFmZtaCljKBaOXv2jOKmBsRWwCDga0lbdKKaS+MiGERMWzllVcuOpmZmbWgpYphSVqLBVf8PSp+t7ovoYj4r6QJwG7AdEmDImKapEGkuwQzM2uQlu4EliW9VrL0WZ70WsnS72WKLETSypJWzMN9gZ2Bp4GbgZE52kjSQ2lmZtYgzd4JRETRJ4pbMggYI6knKeO5JiJukfR34BpJhwOTgX06aHlmZlZAq14v2VYR8U9gyyrhs4ARjUiDmZktqqOu9M3MrAtyJmBm1o05EzAz68acCZiZdWM1K4YlTaHAw2ARMaRDU2RmZg3TXOugr5QNf5zUjv9s4CVgTeBI4PL6Jc3MzOqtZiYQEXeXhiX9Dtg1Il4uC/sL8Ffg9Lqm0Mysgww94dY2TdfU54C2LXD07LZN10BF6wRWA96qCHsLWL1jk2NmZo1UNBO4mfR6yV0kbSjpM8ANOdzMzLqoopnAEcDfgQuAx4DzgQdzuJmZdVFFXy/5LumtX37zl5nZEqTwcwK5KOhiSX/Kv4dJ2ql+STMzs3ordCcg6Sjg28BFwN45eA6pyegn6pM0M2u0Tcds2qbpnhz5ZAenxBql6J3AMcDOEXEKMC+HPQ2sX49EmZlZYxTNBPoBU/Jw6Sni3sD7HZ4iMzNrmKKZwD0sWil8NHBXxybHzMwaqehLZY4C/iTpa0A/Sc8AbwCfr1vKzMys7oo2EZ0m6ePA1sAQUtHQQxExr/kpzcxscVb49ZIREaQHxB6sX3LMzKyRmutKelJEbJiHa3Yr7a6kzcy6rubuBL5WNvyVmrHMzKzLai4T+A2wbR4eHhE/betCJK1BevfAR0jPGVwYEWdJ6g9cDQwFmoB9I+L1ti6n4Uav0Lbp1vLNk5ktHpprIrqepD55+Lh2LudD4LhcvLQt8C1JG5GanY6PiHWB8bhvIjOzhmruTuAm4N+SmoC+ku6pFikidmhpIRExDZiWh9+UNIn0LoI9gOE52hhgAnB8wbSbmVk7NfdmsUMlfYpUVPNx4OKOWKCkocCWpFZGq+YMotQMdZUa04wCRgEMGeKiFDOzjtJsE9GIuA+4T9JSETGmvQuTtBxwPXBMRLwhqdB0EXEhcCHAsGHDqrZSMjOz1ivabcSLktYCkPQRSWMkXSLpI0UXJKk3KQO4MiL+mIOnSxqUxw8CZrQi7WZm1k5FM4HzgLl5+AxS53FBvjpvidIl/8XApIg4o2zUzcDIPDySVA9hZmYNUvSJ4dUjYrKkXsCuwJqkHkRfKTj9J4GDgCclPZHDfgCcAlwj6XBgMrBP0YSbmVn7Fc0E3pC0KrAJMDEi3pK0FOmOoEW5bqFWBcCIgmkwM7MOVjQTOAd4GFiK9IIZSFf3T9chTWZm1iBFexE9VdINwNyIeD4Hvwx8tW4pMzOzumtNL6L/bu63mZl1Pe5F1MysG3MvomZm3Vhz3UbcV/ZzlYi4tjKOpL3rkiozM2uIog+L1eo3qNDDYmZmtnhqtmJY0tp5sEfuNqK8rf/awLv1SpiZmdVfS62DniNVCAt4vmLcq8DoOqTJzMwapKVeRHsASLo7Ij7dmCSZmVmjFKoTcAZgZrZkKvSwWO447pvAp4GBlNUNFHmzmJmZLZ6Ktg46E/g6cA/wMdJ7AVYB7qxTuszMrAGKZgJfAj4bEWcBH+bvPYEd65UwMzOrv6KZwDLAlDw8R9IyEfE06V3BZmbWRRXtQG4S6WXzDwGPAKMlvUHqSdTMzLqoopnAt1nwesljgfOBfsCoeiTKzMwao+j7BB4uG34W2LluKTIzs4YpVCcgacfcbQSSBkkaI+liSR+pb/LMzKyeilYMn8eC4qDTWfBuYXcgZ2bWhRWtE1g9Iibnh8Z2BdYE3gdeqVvKzMys7oreCbwhaVXSE8MTI+KtHN67mWnmk3SJpBmSnioL6y/pDknP5u+VWpd0MzNrr6KZwDnAw8CVwO9y2CeBpwtOfxmwW0XYCcD4iFgXGJ9/m5lZAxVtHXSqpBuAuRFR6lL6ZeCrBae/R9LQiuA9gOF5eAwwATi+yPzMzKxjFK0TICL+XRqWtCMpQ7inHcteNSKm5XlPk7RKrYiSRpGfSRgyxO+1NzPrKEWbiN4t6ZN5+HhgHDBW0g/qmbiSiLgwIoZFxLCVV165EYs0M+sWitYJbAI8kIe/RirG2RY4oh3Lni5pEKRnD4AZ7ZiXmZm1QdFMoAcQktYBFBGTImIK0J4WPTcDI/PwSOCmdszLzMzaoGidwH3AucAg4AaAnCH8p8jEksaS7h4GSpoKnAScAlwj6XBgMrBPq1JuZmbtVjQTOAQ4DpgJnJbDNgDOKjJxROxfY9SIgss3M7M6KNpEdBbwg4qwW+uSIjMza5iamYCkH0bEL/Pwz2rFi4if1CNhZmZWf83dCQwuG16j3gkxM7PGq5kJRMQ3yoYPbUxyzMyskQrVCUjaCNge6A+8BtwbERPrmTAzM6u/ZjMBSQIuJrXjn0rqOnp1YDVJVwCHRUTUPZVmZlYXLT0sNor8dHBErBkR20XEEGA70p3B1+ucPjMzq6OWMoGDgKPL3zEM8985fEweb2ZmXVRLmcBGwN01xt2dx5uZWRfVUsVwz4h4s9qIiHhTUtG+h8zqYtMxm7ZpuidHPtnBKTHrmlrKBHrndweojdObmdlirKWT+AzgkhbGm5lZF9VsJhARQxuUDjMz6wQu0zcz68acCZiZdWPOBMzMujFnAmZm3ZgzATOzbsyZgJlZN+ZMwMysG3MmYGbWjXV6JiBpN0nPSHpO0gmdnR4zs+6kUzMBST2B3wGfJfVIun9+i5mZmTVAZ98JbA08FxEvRMT7wDhgj05Ok5lZt6HOfDukpL2B3SLiq/n3QcA2EXFkRbxRpLecAawPPNPQhLbNQOA/nZ2IJYi3Z8fxtuxYXWV7rhkRK1cGdnZX0NW6qF4kV4qIC4EL65+cjiPpkYgY1tnpWFJ4e3Ycb8uO1dW3Z2cXB00F1ij7PZj0MnszM2uAzs4EHgbWlbSWpKWA/YCbOzlNZmbdRqcWB0XEh5KOBG4DegKXRMS/OjNNHahLFV91Ad6eHcfbsmN16e3ZqRXDZmbWuTq7OMjMzDqRMwEzs27MmUArSQpJp5f9/q6k0Z2YpMWSpAmSdq0IO0bSeTXiHyNpmcakbvEgaa6kJ8o+de02RdIXOqtrFklNkgY2aFmFt6ukPYv0UiDpsvxcU2X4cEm35OFmt6+kQySdW3Q9mltWR+rs5wS6oveAL0k6OSK6wgMinWUsqbXXbWVh+wHfqxH/GOAPwDtFFyCpZ0TMbWsCFwNzImKLRixIUq+IuJnu0fquNdt1T+AWYGJ7F9pVt6/vBFrvQ1JrgO9UjpC0sqTrJT2cP5/M4U9KWlHJLEkH5/ArJO3c2OQ3zHXA7pKWBpA0FFgNWEbS3yU9JulaSctJOjqPu0vSXTn+Zyrj5fAmST+RdB+wj6Sv5W39j7ztl8nxLpN0tqS/SXqh2lXc4kjSCrlDxfXz77GSvpaH35J0et4m4yWtnMPXkfRXSY9KulfSBjn8Mkln5G16avmVaDP76mhJl+Q7uRfyf1NK28GS/pm39RUtzGeApNslPS7p91R/MLShJJ0iaWJeh99I+gTwBeC0fMewTq39Kds5b99/S9q9yvzLt+8+kp7K87mnLNpq+b96VtKvy6attb/vJunpvL9/qS4bJiL8acUHeAtYHmgCVgC+C4zO464CPpWHhwCT8vAFwOeATUjPRvxvDn8WWK6z16mO2+pWYI88fAJwKXAPsGwOOx74SR5uAgbm4YEtxPt+2TIGlA3/AjgqD18GXEu60NmI1EdVp2+Tiu0zF3ii7PPlHL4L8HfSndNfy+IHcGAe/glwbh4eD6ybh7cB7izbBrcAPfPvQ8qmqbWvjgb+Biyd/4dZQG9gY1J3LaX/qH8L8zm77D/7XE77wM7arkD/nP5Si8gVy7bR3gX3p7/m/Wld0oOufYDhwC1Vtu+TwOoVyzoEeIF03ugDvER6WLbq/p7jTMnLE3BNaVkd+XFxUBtExBuSLgeOBuaUjdoZ2Eiaf9GzvKR+wL3ADqQ//XxglKTVgdci4q3GpbzhSkVCN+XvPwKfB+7P22gp0smu0rakE3eteFeXDW8i6RfAisByLFz8dGNEzAMmSlq1A9ano1UttoiIOyTtQ+phd/OyUfNYsO5/AP6Yrxg/AVxbtt8tXTbNtVG9yKzWvgpwa0S8B7wnaQawKrATcF3kItCIeK2F+exAvnKNiFslvd7sluhYi2xXSb2Ad4GLJN1KyhyraW5/uibvT89KegHYoJk03A9cJuka0n5fMj4iZuc0TQTWzMuqtr9vALwYEc/m+H9gQR9qHcaZQNv9FniMdHVb0gPYLiLKMwby7eC3SFdKPwS+COxNyhyWZDcCZ0jaCugLPA7cERH7tzCdWoj3dtnwZcCeEfEPSYeQrsxK3quYZ5cgqQewIekCoz/pqrOaIO1z/62WmWRv1wivta/CwtttLuk8Iar069XCfBabh5AiPZi6NTCCdEFyJCljq3QZtfenyvWpuX4RcYSkbUh3QU9I2iKPqrVtF9nf8zR134auE2ijfCV0DXB4WfDtpJ0LmP8nEhFTSLd860bEC8B9pGKkJToTyHc5E4BLSHcFDwCflPRRAEnLSFovR38TKF2JNhevUj9gmqTewIF1WZHG+w4wCdgfuCSvG6TjtVS3cQBwX0S8AbyY7xxQsnnlDKuouq82Yzywr6QBOX7/FuZzD/n/kPRZYKUCaaqbfMe0QkT8mdQIYYs8qny/g+b3p30k9ZC0DrA2zfRmLGmdiHgwIn5C6mF0jVpxqb2/Pw2slZcHaX/ocM4E2ud00sm95GhgWK54mggcUTbuQeDfefheYHVSZrCkG0sq0hgXETNJ5aJjJf2TtPOXbqkvBP4i6a4W4lX6MWnb3kE6aLqSvlq4KeMp+eD/KnBcRNxLOpn+KMd/G9hY0qOkq9if5fADgcMl/QP4F8XeydHcvrqISN25/BK4Oy/njBbm81NgB0mPAZ8BJhdIU0dZZLuSTu635P3pbhY07BgHfC9XYK9D8/vTM3navwBHRMS7zaThNKUGIU+R/sN/1IpYa3/P8x8F3Jorhl9qxTYozN1GmHURkt6KiOU6Ox22ZPGdgJlZN+Y7ATOzbsx3AmZm3ZgzATOzbsyZgJlZN+ZMwLodSf+SNLyz02G2OHAmYIsFSZ9S6uxttqTXJN0v6eP1WFZEbBwRE+ox79ZS6qjtq52dDuu+3G2EdTpJy5P6cvkG6SnspYDtWfgR+6Lz6hURH3ZsCpc8Sv06KPeFY92Y7wRscbAeQESMjYi5ETEnIm6PiH+WIkg6TNIkSa9Luk3SmmXjQtK3JD1L6tzrAkm/KV+ApJskHZuHm5S78JbUU9IPJD0v6U2l7pjXyOM2kHRHvjN5RtK+tVZAUn9Jl0p6Jafxxhy+kqRbJM3M4bdIGpzH/ZKU2Z2r1E30uS0tV6mL5j9JekOpy+Nf5KdJS+M/kcNn5+9PlI2bIOmXku4nvbfhuPz0cfl6HFdKu3UTjeja1R9/mvuQuuaeBYwBPgusVDF+T+A5UqdqvUjdKPytbHyQHvPvT+qobgdSF7yl52BWInXGtlr+3QTsnIe/R+r2d31SR16bAwOAZfM8Ds3L3IrUB8zGNdbhVlIPnyuRul7+dA4fAOwFLEPquuBaUu+mpekmAF8t+93sckndHIzL89sox70vj+sPvA4clKfdP/8eULasyaRuoXuReht9DdiwbPmPA3t19j7hT+M+nZ4Af/yJCPIJ/jJSj5kfkt7QtGoe9xfg8LK4PUhXsmvm3wHsVDZe+WS3Q/79NXIf+/l3eSbwDPmdBxXp+TJwb0XY74GTqsQdROrmeaUC67kF8HrZ78pMoOZygZ7AB8D6ZeN+UZYJHAQ8VDHt34FDypb1s4rx5wO/zMMb50xj6c7eH/xp3MfFQbZYiIhJEXFIRAwmvXxnNVJ33ZD6XD9L0n8l/Zd09SpSJ3wlU8rmFaSr5VKviwcAV9ZY9BrA81XC1wS2KS0zL/dA4CM15vFaRCzSZ37uEfL3kl6S9AapM7EVJfWskZ7mlrsy6Qp+Sln88uHVWLSTsZeosZ2yMcABuY7gIFKf+a2ui7Guy5mALXYi4mnSXcEmOWgK8PWIWLHs0zci/lY+WcVsxgJ757qDbYDrayxuCrBOjfC7K5a5XER8o0bc/pJWrDLuOFJR0zYRsTypqAoWvN+gMt3NLXcm6S5pcFn88i6KXyFlIuWGAC+X/V5oeRHxAPA+qW7iAOCKKutgSzBnAtbpckXocWUVpmuQruIfyFEuAE6UtHEev4Jy//m1RMTjpJPmRcBtEfHfGlEvAn4uaV0lmyn1mX8LsJ6kgyT1zp+PS9qwyrKmkYqszssVwb0llU72/Uj1Ef9V6oP/pIrJp5P6pi+pudxIbwj7IzA632FsABxcNu2f87QHSOol6cukeoNab9EquRw4F/gwIrpD9+ZWxpmALQ7eJF2tPyjpbdLJ/ynSVTQRcQNwKjAuF6k8RapAbslY0usPr2omzhmkZqm3A28AFwN9I+JNUj/4+5GusF/NaVi6xnwOIpXXPw3MIL24BFKRVl9S5e4DpPfUljuLdMfyuqSzCyz3SNI7al8lXbWPJTeljYhZwO6k7TYL+D6we+RXQjbjCtJdl+8CuiH3ImrWhUk6FfhIRIxsxzz6kjKurSK/z9a6D98JmHUhuehss1x0tTXp9aY3tHO23wAedgbQPfmJYbOupR+pCGg10tX76cBNbZ2ZpCZSJfWeHZA264JcHGRm1o25OMjMrBtzJmBm1o05EzAz68acCZiZdWPOBMzMurH/Bw1lY7FthyftAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "diss_sc.plot(kind='bar', rot = 0)\n", "plt.title('Dissatisfied by Service Category', fontsize=20)\n", "plt.xlabel('Service category', fontsize=12)\n", "plt.ylabel('Dissatisfied Employees Number', fontsize=12)\n", "plt.legend()\n", "# use sns.despine() and frameon = False to remove any borders" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Analysing the total dissatisfied columns, we can see that the Veteran,New and Experienced contribute in almost same proportions, at about 30%.\n", "\n", "- In DETE Institute, the maximum percentage of dissatisfied employees are Veteran at 42.28 % while in TAFE Institute, the maximum percentage of dissatisfied employees are New at 55%.\n", "\n", "- In every service category, DETE dominates as the institute with the higher number of employees leaving due to dissatisfaction, except for the New category.\n", "\n", "- Overall, 73% of the employees left the DETE institute because of dissatisfaction issues, while only 27% of the employees from the TAFE institute left because of dissatsifaction." ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcease_datepositionemployment_statusgenderageinstitute_servicedissatisfiedinstituteservice_catage_cat
3086.341466e+172010.0Teacher (including LVT)Permanent Full-timeMale43.04.0FalseTAFEExperiencedMiddle
3096.341475e+172010.0Teacher (including LVT)Contract/casualFemale56.00.0FalseTAFENewSenior
3106.341520e+172010.0Administration (AO)Temporary Full-timeMale20.04.0FalseTAFEExperiencedJunior
3116.341537e+172010.0Teacher (including LVT)Permanent Full-timeMale48.04.0FalseTAFEExperiencedSenior
3126.341579e+172009.0TutorTemporary Full-timeFemale38.04.0FalseTAFEExperiencedMiddle
....................................
5936.350599e+172013.0Administration (AO)Temporary Full-timeFemale28.02.0FalseTAFENewJunior
5946.350660e+172013.0Operational (OO)Temporary Full-timeMale23.06.0FalseTAFEExperiencedJunior
5956.350668e+172013.0Teacher (including LVT)Temporary Full-timeMale53.02.0FalseTAFENewSenior
5966.350704e+172013.0Teacher (including LVT)Permanent Full-timeFemale53.06.0FalseTAFEExperiencedSenior
5976.350730e+172013.0Administration (AO)Contract/casualFemale28.04.0FalseTAFEExperiencedJunior
\n", "

290 rows × 11 columns

\n", "
" ], "text/plain": [ " id cease_date position employment_status \\\n", "308 6.341466e+17 2010.0 Teacher (including LVT) Permanent Full-time \n", "309 6.341475e+17 2010.0 Teacher (including LVT) Contract/casual \n", "310 6.341520e+17 2010.0 Administration (AO) Temporary Full-time \n", "311 6.341537e+17 2010.0 Teacher (including LVT) Permanent Full-time \n", "312 6.341579e+17 2009.0 Tutor Temporary Full-time \n", ".. ... ... ... ... \n", "593 6.350599e+17 2013.0 Administration (AO) Temporary Full-time \n", "594 6.350660e+17 2013.0 Operational (OO) Temporary Full-time \n", "595 6.350668e+17 2013.0 Teacher (including LVT) Temporary Full-time \n", "596 6.350704e+17 2013.0 Teacher (including LVT) Permanent Full-time \n", "597 6.350730e+17 2013.0 Administration (AO) Contract/casual \n", "\n", " gender age institute_service dissatisfied institute service_cat \\\n", "308 Male 43.0 4.0 False TAFE Experienced \n", "309 Female 56.0 0.0 False TAFE New \n", "310 Male 20.0 4.0 False TAFE Experienced \n", "311 Male 48.0 4.0 False TAFE Experienced \n", "312 Female 38.0 4.0 False TAFE Experienced \n", ".. ... ... ... ... ... ... \n", "593 Female 28.0 2.0 False TAFE New \n", "594 Male 23.0 6.0 False TAFE Experienced \n", "595 Male 53.0 2.0 False TAFE New \n", "596 Female 53.0 6.0 False TAFE Experienced \n", "597 Female 28.0 4.0 False TAFE Experienced \n", "\n", " age_cat \n", "308 Middle \n", "309 Senior \n", "310 Junior \n", "311 Senior \n", "312 Middle \n", ".. ... \n", "593 Junior \n", "594 Junior \n", "595 Senior \n", "596 Senior \n", "597 Junior \n", "\n", "[290 rows x 11 columns]" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined[combined['institute']== 'TAFE']" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'TAFE')" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(18,10), dpi=1600)\n", "\n", "ax1 = plt.subplot2grid((2,4),(0,0))\n", "plt.pie(diss_sc.iloc[:,1], labels = diss_sc.index, colors = ('b','g','y','r'))\n", "plt.title('DETE')\n", "\n", "ax1 = plt.subplot2grid((2, 4), (0, 1))\n", "plt.pie(diss_sc.iloc[:,2], labels = diss_sc.index, colors = ('b','g','y','r'))\n", "plt.title('TAFE')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## " ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dissatisfiedDETETAFE
age_cat
Senior946529
Middle835528
Junior492920
\n", "
" ], "text/plain": [ " dissatisfied DETE TAFE\n", "age_cat \n", "Senior 94 65 29\n", "Middle 83 55 28\n", "Junior 49 29 20" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diss_ac = combined.pivot_table(index='age_cat', values='dissatisfied', aggfunc=np.sum)['dissatisfied'].sort_values(ascending=False).to_frame()\n", "\n", "DETE_sc = combined[combined['institute'] == 'DETE'].reset_index(drop=True)\n", "diss_ac['DETE'] = DETE_sc.pivot_table(index='age_cat', values='dissatisfied', aggfunc=np.sum)['dissatisfied'].sort_values(ascending=False)\n", "\n", "TAFE_sc = combined[combined['institute'] == 'TAFE'].reset_index(drop=True)\n", "diss_ac['TAFE'] = TAFE_sc.pivot_table(index='age_cat', values='dissatisfied', aggfunc=np.sum)['dissatisfied'].sort_values(ascending=False)\n", "\n", "diss_ac" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dissatisfiedDETETAFE
Service Category
Senior946529
Middle835528
Junior492920
\n", "
" ], "text/plain": [ " dissatisfied DETE TAFE\n", "Service Category \n", "Senior 94 65 29\n", "Middle 83 55 28\n", "Junior 49 29 20" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diss_ac.index.name = 'Service Category'\n", "diss_ac" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PbTbFhYBZM+QLt6aSzlQ3izr9gHKHcK+IeL0sfBdS88iUiNisHnlpbyStFWXNb5IGkJqGPiRdtfx+xZVbPy93k5p4t4sKk/C1BfcJmBUgaQRpArfhpA7/X9SrAMhWIF2j8SCpGexjUp/RbqQ/su/VMS/tzQRJL5Caj+aT+i6+RurvO7bWBUAucPYiDfveA7hzaSkAwDUBs0Jyh/2OpBlgz4uI8+u8/U6kzvgvkzrPu5L6qh4GzorK0xUYIOlUUj9EP1I/1LukfrlzImJ8HbY/ktSX9h6p1vbdaHpQSd24EDAz68DaXXPQGmusEf369WvrbJiZtSsTJ058KyI+M+FluysE+vXrx4QJE5qOaGZmC0l6qVK4p40wM+vAXAiYmXVgLgTMzDqwdtcnYGbty0cffcTMmTN5//26XI/V4XXp0oW+ffuy/PLFbi7oQsDMamrmzJl0796dfv360cj9E6wVRASzZ89m5syZrLfeeoXWcXOQmdXU+++/T8+ePV0A1IEkevbs2axalwsBM6s5FwD109x97ULAzKwDc5+AmdVVv5PvatX0pp/1tWbFHzVqFN26deO9995jxx13ZNddd13yPEyfzuOPP86IESMAmDBhAtdccw0XXlj9Fto//vGPufvuu9lzzz3ZYIMN6Nq1K4ccckjhbXbr1o158+Ytcd5dCDShtQ7Y5h6oZlZbp59+equlNX36dK6//vqFhcCQIUMYMmRIo+tcdtllzJo1ixVXXLHReLXm5iAzW+adeeaZbLzxxuy6665MmZLuuzNy5EhuvPFGAE4++WQ23XRTBg4cyI9+9CMAxo0bx+abb84WW2zBjjvuCKQ/+6FDhzJ48GAGDx7M448/vnD9Rx55hEGDBnHeeecxfvx49tprLwAeeughBg0axKBBg9hyyy2ZO3cue++9N/Pnz2fbbbflhhtuYNSoUZxzzjkATJs2jd13352tttqKoUOHMnnyZAD+9a9/sf3227P11lvzi1/8otX2jWsCZrZMmzhxImPHjuXpp5/m448/ZvDgwWy11VYLl7/99tvccsstTJ48GUm8++67QKop3HPPPfTp02dhWK9evbjvvvvo0qULU6dO5aCDDmLChAmcddZZnHPOOdx5550AjB8/fmH655xzDhdffDE77LAD8+bNo0uXLtx+++1069aNZ555BkhNVA2OPvpoLr30UjbccEOefPJJvvvd7/LAAw9wwgkncNxxx3HIIYdw8cUXt9r+cU3AzJZpjzzyCN/85jfp2rUrPXr0YO+9915seY8ePejSpQtHHnkkN998M127dgVghx12YOTIkfz+97/nk08+AdKFb0cddRQDBgxg//335/nnn29y+zvssAMnnngiF154Ie+++y6dO1c/9543bx6PP/44+++/P4MGDeKYY47htddeA+Cxxx7joIMOAuDgg1vvzpRNFgKSOkkaL6ltG67MzFqosWGTnTt35qmnnmLffffl1ltvZffddwfg0ksv5YwzzmDGjBkMGjSI2bNnc95557Hmmmvy7LPPMmHCBD788MMmt33yySdz+eWXs2DBArbbbruFzTuVfPrpp6y66qo888wzCx+TJk0q9DlaqslCICI+AdYrEtfMbGmz4447csstt7BgwQLmzp3LHXfcsdjyefPmMWfOHPbcc0/OP//8hU0006ZNY9ttt+X0009njTXWYMaMGcyZM4fevXuz3HLLce211y6sIXTv3p25c+dW3P60adMYMGAAP/3pTxkyZEijhUCPHj1Yb731GDduHJCuAH722WeBVKMYO3YsANddd90S7ZNSRfsETgN+l2/TNhNYeDuyiPi01XJjZsu8eo+UGzx4MAceeCCDBg1i3XXXZejQoYstnzt3Lvvssw/vv/8+EcF5550HpCGcU6dOJSLYZZdd2GKLLfjud7/Lvvvuy7hx49h5551ZeeWVARg4cCCdO3dmiy22YOTIkWy55ZYL0z///PN58MEH6dSpE5tuuil77LFHo/m97rrrOO644zjjjDP46KOPGD58OFtssQUXXHABI0aM4IILLmDfffdttf1T6PaSkhr+6EsjC4iI6NRquSlgyJAhUc+byniIqNmSmTRpEv3792/rbHQolfa5pIkR8Zlxq0VrAsVmIjIzs3alUCEQES8BSFoOWDMiXqtprszMrC4KdfZKWlXS9cD7wAs5bG9JZ9Qyc2ZmVltFR/xcCswB1gUaxkQ9ARxYi0yZmVl9FO0T2AVYKyI+khQAETFLUq/aZc3MzGqtaE1gDrBGaYCkdQD3DZiZtWNFawKXAzdJ+k9gOUnbA78iNROZmRU3apVWTm9Ok1E6derEgAED+Oijj+jcuTOHHnooP/zhD1luueUYP348++yzz2K3YzzllFP49a9/DcDrr79Op06d+NznPgfAU089xUorrcSAAQMWxh8+fDgnn3xy636uOilaCJxN6hS+GFgeuBK4DLigRvkyM2s1K6200sIrgd98801GjBjBnDlzOO200wAYOnTowsnfGhx4YOrybLj/QMPsouXptXeFmoMiOT8iNo2IlSOif37f9JVmZmZLkV69ejF69Gguuugi/BfWjKmkJX0ZOAhYC3gVGBsR99cqY2ZmtbL++uvz6aef8uabbwIsvBdAg5tuuokNNtig6voLFixYLP4pp5yysObQ3hQqBCSdCJwMXAU8DawDXC/pvyPi3Brmz8ysJkprAZWagxqzLDUHFa0JnAR8OSL+0RAg6VrgPsCFgJm1Ky+++CKdOnWiV69ei03V3BE1Z3roF8rev8jiE8qZmS31Zs2axbHHHsv3v//9mszP395UrQnkeYIajAKukDSKNJX02sAvgFNrmTkzWwYVGNLZ2hra8BuGiB588MGceOKJC5eX9wn8/Oc/Z7/99msyvQa77747Z511Vi2yXnONNQd9zKIz/Ybi8qCysBGkawjMzJZaDTd/qWTYsGHMmVO9YCq9/2+R9NqbxgoBTx9tZraMq1oINEwfbdZetdYNgcA3BbJlV9EhoqsAxwNbAt1Kl0XEV2qQLzMzq4OiQ0THAZ2AW4AFtcuOmZnVU9FCYDugZ0R81NINSfoP4EhSx/JzwGFAV+AGoB8wHTggIt5p6TbMzKx5il4n8CjQ4jtFS+pDak4aEhGbk2oVw0lXId8fERsC9+f3ZmZWJ0VrAiOBuyU9CbxRuiAiTm/GtlaS9BGpBvAqcAowLC+/GhgP/LRgembWDg24ekDTkZrhuUOfa3T57Nmz2WWXXYDPTgt9zz330LdvXy666CKOOeaYhev069eP7t2706lTJwAuueQS1lprLfr378/GG2+8MN6JJ57IIYcc0qqfp96KFgJnki4Qmw70KAkvdMVwRLwi6RzgZVKfwr0Rca+khTetj4jXfKcyM2ttPXv2XDjPT/m00JdccgnbbbcdY8aMWawQAHjwwQdZY41F99KaPn06G2ywwTIzZ1CDooXAcGCjhj/s5pK0GrAP6dqDd4Fxkr7TjPWPBo4GWGeddVqSBTOzzxgzZgznnnsuI0aM4JVXXqFPnz5tnaW6K9on8CLQ4k5hYFfgXxExK3cu3wx8EXhDUm+A/PxmpZUjYnREDImIIQ3VODOzJTFjxgxef/11ttlmGw444ABuuOGGxZbvvPPODBo0iG233XZh2LRp0xg0aNDCxyOPPFLvbLe6ojWBa4HbJf2Wz/YJPFBg/ZeB7SR1JTUH7QJMAOYDhwJn5efbCubHzGyJjB07lgMOOABIt4c84ogjFptPqLw5COjQzUHfy8+/KgsPYP2mVo6IJyXdCPyNNCfR08Bo0oVnf5R0BKmg2L9gfszMlsiYMWN44403uO666wB49dVXmTp1KhtuuGEb56y+ChUCEbHE8whFxKl8dtbRD0i1AjOzupkyZQrz58/nlVdeWRh26qmnMnbsWH7xi1+0Yc7qr/DtJc3MWkNTQzrrYcyYMXzzm99cLGzfffdl+PDhjRYCDX0CDQ4//HCOP/74WmWzLorOHTSDKsNBI8LDdcysXag0LXSDgQMH8vzzzwNpOGi5fv36sWDBsjdrTtGaQPlwzt7ACcDY1s2OmZnVU9E+gYfKwySNB/4MXNDKeTIzszppzj2Gy32AbzxjZgVE+Hbk9dLcfV20T6B8fqCuwJ7An5q1NTPrcLp06cLs2bPp2bOnb+xeYxHB7Nmz6dKlS+F1ivYJrF32fj7wG9JFZGZmVfXt25eZM2cya9asts5Kh9ClSxf69u1bOH7RPoHDWpwjM+vQll9+edZbzy3HS6tGCwFJOzaVQEQ83HrZMTOzemqqJnBdlfAAVgVWJt0gxszM2qFGC4GIKO8LQNKawM9It4e8tEb5MjOzOig8RFTSqpJ+DUwBugMDI+J7TaxmZmZLsSYLAUndJP0S+BdpxtBtI+LwiJhe68yZmVltNVoISDqJdEOZrYCdIuLAiJhSl5yZmVnNNdUx/P+At4HVgYsqXegREU2OIDIzs6VTU4WArw8wM1uGNTU66Op6ZcTMzOpvSSaQMzOzds6FgJlZB+ZCwMysA3MhYGbWgRUqBCSdKGlQfr2dpJclvShp+5rmzszMaqpoTeA/SFcMA/yadC+BM4Hza5AnMzOrk6I3lVklIuZI6g5sAewaEZ9IOreGeTMzsxorWgjMkPRFYDPg4VwA9AA+qV3WzMys1ooWAj8GbgQ+BPbNYXsBT9UiU2ZmVh9Fby95N7BWWfC4/DAzs3aqaE0ASf2B/YA1I+L7wAbACsDfa5Q3MzOrsUKFgKT9gUuAm4ARwPeBbsBZwK41y92yZNQqrZjWnNZLy8w6tKJDRE8HdouIY1nUGfwsaaSQmZm1U0ULgV6kP31IN5lveI7K0c3MrD0oWghMBA4uCxuORweZmbVrRTuGjwfulXQEsLKke4CNgK/ULGdmZlZzRYeITpa0CenagDuBGcCdETGvlpkzM7PaKjxENCL+LekJ4OWI+EsN82RmZnVSdBbRdSQ9BkwG/jeH7Sfp8qIbkrSqpBslTZY0SdL2klaXdJ+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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "diss_ac.plot(kind='bar', rot = 0)\n", "plt.title('Dissatisfied by Age Category', fontsize=20)\n", "plt.xlabel('Service category', fontsize=12)\n", "plt.ylabel('Dissatisfied Employees Number', fontsize=12)\n", "plt.legend()\n", "# use sns.despine() and frameon = False to remove any borders" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Analysing the total dissatisfied columns, we can see that the Senior and Middle age groups are the most dissatisfied with their jobs. Almost 80% of the employees who left due to dissatisfaction are from the above age groups.\n", "\n", "- In DETE Institute, the maximum percentage of dissatisfied employees are Senior at 43.6 % while in TAFE Institute, the maximum percentage of dissatisfied employees are Senior and Middle at about 37% each.\n", "\n", "- In every service category, DETE dominates as the institute with the higher number of employees leaving due to dissatisfaction, as seen in the pie charts below.\n" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'JUNIOR')" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(18,10), dpi=1600)\n", "\n", "ax1 = plt.subplot2grid((2,4),(0,0))\n", "plt.pie(diss_ac.iloc[0,1:3], labels = diss_ac.columns[1:3], colors = ('orange','g'))\n", "plt.title('SENIOR')\n", "\n", "ax1 = plt.subplot2grid((2, 4), (0, 1))\n", "plt.pie(diss_ac.iloc[1,1:3], labels = diss_ac.columns[1:3], colors = ('orange','g'))\n", "plt.title('MIDDLE')\n", "\n", "ax1 = plt.subplot2grid((2, 4), (0, 2))\n", "plt.pie(diss_ac.iloc[2,1:3], labels = diss_ac.columns[1:3], colors = ('orange','g'))\n", "plt.title('JUNIOR')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "- Generally, the people who have served for more number of years or the ones who are older inn general are the ones to leave due to dissatisfaction in majority.\n", "\n", "\n", "- Overall, 73% of the employees left the DETE institute because of dissatisfaction issues, while only 27% of the employees from the TAFE institute left because of dissatsifaction." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }