diff --git a/EDA--improved/LICENSE b/EDA--improved/LICENSE new file mode 100644 index 0000000..3a8876d --- /dev/null +++ b/EDA--improved/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Debanik21 + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Stackoverflow_Survey_Analysis-checkpoint.ipynb b/Stackoverflow_Survey_Analysis-checkpoint.ipynb new file mode 100644 index 0000000..bb4aa50 --- /dev/null +++ b/Stackoverflow_Survey_Analysis-checkpoint.ipynb @@ -0,0 +1,18861 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stackoverflow_Survey_Analysis_2018" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "import warnings; \n", + "warnings.simplefilter('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exlporing 2018 Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RespondentHobbyOpenSourceCountryStudentEmploymentFormalEducationUndergradMajorCompanySizeDevType...ExerciseGenderSexualOrientationEducationParentsRaceEthnicityAgeDependentsMilitaryUSSurveyTooLongSurveyEasy
01YesNoKenyaNoEmployed part-timeBachelor’s degree (BA, BS, B.Eng., etc.)Mathematics or statistics20 to 99 employeesFull-stack developer...3 - 4 times per weekMaleStraight or heterosexualBachelor’s degree (BA, BS, B.Eng., etc.)Black or of African descent25 - 34 years oldYesNaNThe survey was an appropriate lengthVery easy
13YesYesUnited KingdomNoEmployed full-timeBachelor’s degree (BA, BS, B.Eng., etc.)A natural science (ex. biology, chemistry, phy...10,000 or more employeesDatabase administrator;DevOps specialist;Full-......Daily or almost every dayMaleStraight or heterosexualBachelor’s degree (BA, BS, B.Eng., etc.)White or of European descent35 - 44 years oldYesNaNThe survey was an appropriate lengthSomewhat easy
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2 rows × 129 columns

\n", + "
" + ], + "text/plain": [ + " Respondent Hobby OpenSource Country Student Employment \\\n", + "0 1 Yes No Kenya No Employed part-time \n", + "1 3 Yes Yes United Kingdom No Employed full-time \n", + "\n", + " FormalEducation \\\n", + "0 Bachelor’s degree (BA, BS, B.Eng., etc.) \n", + "1 Bachelor’s degree (BA, BS, B.Eng., etc.) \n", + "\n", + " UndergradMajor \\\n", + "0 Mathematics or statistics \n", + "1 A natural science (ex. biology, chemistry, phy... \n", + "\n", + " CompanySize \\\n", + "0 20 to 99 employees \n", + "1 10,000 or more employees \n", + "\n", + " DevType ... \\\n", + "0 Full-stack developer ... \n", + "1 Database administrator;DevOps specialist;Full-... ... \n", + "\n", + " Exercise Gender SexualOrientation \\\n", + "0 3 - 4 times per week Male Straight or heterosexual \n", + "1 Daily or almost every day Male Straight or heterosexual \n", + "\n", + " EducationParents RaceEthnicity \\\n", + "0 Bachelor’s degree (BA, BS, B.Eng., etc.) Black or of African descent \n", + "1 Bachelor’s degree (BA, BS, B.Eng., etc.) White or of European descent \n", + "\n", + " Age Dependents MilitaryUS \\\n", + "0 25 - 34 years old Yes NaN \n", + "1 35 - 44 years old Yes NaN \n", + "\n", + " SurveyTooLong SurveyEasy \n", + "0 The survey was an appropriate length Very easy \n", + "1 The survey was an appropriate length Somewhat easy \n", + "\n", + "[2 rows x 129 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2018 = pd.read_csv(r'survey_results_public_2018.csv')\n", + "df2018.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataset contains 98855 rows and 129 columns.\n", + "Respondent int64\n", + "Hobby object\n", + "OpenSource object\n", + "Country object\n", + "Student object\n", + " ... \n", + "Age object\n", + "Dependents object\n", + "MilitaryUS object\n", + "SurveyTooLong object\n", + "SurveyEasy object\n", + "Length: 129, dtype: object\n", + " Respondent AssessJob1 AssessJob2 AssessJob3 AssessJob4 \\\n", + "count 98855.000000 66985.000000 66985.000000 66985.000000 66985.000000 \n", + "mean 50822.971635 6.397089 6.673524 5.906875 4.065791 \n", + "std 29321.650410 2.788428 2.531202 2.642734 2.541196 \n", + "min 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "25% 25443.500000 4.000000 5.000000 4.000000 2.000000 \n", + "50% 50823.000000 7.000000 7.000000 6.000000 4.000000 \n", + "75% 76219.500000 9.000000 9.000000 8.000000 6.000000 \n", + "max 101592.000000 10.000000 10.000000 10.000000 10.000000 \n", + "\n", + " AssessJob5 AssessJob6 AssessJob7 AssessJob8 AssessJob9 \\\n", + "count 66985.000000 66985.000000 66985.000000 66985.000000 66985.000000 \n", + "mean 3.953243 4.407196 5.673181 4.225200 7.640009 \n", + "std 2.520499 2.502069 2.923998 2.507411 2.407457 \n", + "min 1.000000 1.000000 1.000000 1.000000 1.000000 \n", + "25% 2.000000 2.000000 3.000000 2.000000 6.000000 \n", + "50% 3.000000 4.000000 6.000000 4.000000 8.000000 \n", + "75% 6.000000 6.000000 8.000000 6.000000 10.000000 \n", + "max 10.000000 10.000000 10.000000 10.000000 10.000000 \n", + "\n", + " ... JobEmailPriorities6 JobEmailPriorities7 ConvertedSalary \\\n", + "count ... 46213.00000 46213.000000 4.770200e+04 \n", + "mean ... 4.97425 4.836388 9.578086e+04 \n", + "std ... 1.86063 1.659844 2.023482e+05 \n", + "min ... 1.00000 1.000000 0.000000e+00 \n", + "25% ... 4.00000 4.000000 2.384400e+04 \n", + "50% ... 5.00000 5.000000 5.507500e+04 \n", + "75% ... 7.00000 6.000000 9.300000e+04 \n", + "max ... 7.00000 7.000000 2.000000e+06 \n", + "\n", + " AdsPriorities1 AdsPriorities2 AdsPriorities3 AdsPriorities4 \\\n", + "count 60479.000000 60479.000000 60479.000000 60479.000000 \n", + "mean 2.726880 3.805784 3.340945 3.782470 \n", + "std 1.881078 1.821323 1.673485 1.844864 \n", + "min 1.000000 1.000000 1.000000 1.000000 \n", + "25% 1.000000 2.000000 2.000000 2.000000 \n", + "50% 2.000000 4.000000 3.000000 4.000000 \n", + "75% 4.000000 5.000000 5.000000 5.000000 \n", + "max 7.000000 7.000000 7.000000 7.000000 \n", + "\n", + " AdsPriorities5 AdsPriorities6 AdsPriorities7 \n", + "count 60479.000000 60479.000000 60479.000000 \n", + "mean 4.383604 5.138809 4.821459 \n", + "std 1.931746 1.853249 1.874895 \n", + "min 1.000000 1.000000 1.000000 \n", + "25% 3.000000 4.000000 3.000000 \n", + "50% 5.000000 6.000000 5.000000 \n", + "75% 6.000000 7.000000 7.000000 \n", + "max 7.000000 7.000000 7.000000 \n", + "\n", + "[8 rows x 42 columns]\n", + "Missing values per column:\n", + "Country 412\n", + "Student 3954\n", + "Employment 3534\n", + "FormalEducation 4152\n", + "UndergradMajor 19819\n", + " ... \n", + "Age 34281\n", + "Dependents 36259\n", + "MilitaryUS 83074\n", + "SurveyTooLong 32914\n", + "SurveyEasy 32976\n", + "Length: 126, dtype: int64\n", + "Column Hobby has 2 unique values.\n", + "Hobby\n", + "Yes 79897\n", + "No 18958\n", + "Name: count, dtype: int64 \n", + "\n", + "Column OpenSource has 2 unique values.\n", + "OpenSource\n", + "No 55769\n", + "Yes 43086\n", + "Name: count, dtype: int64 \n", + "\n", + "Column Country has 183 unique values.\n", + "Country\n", + "United States 20309\n", + "India 13721\n", + "Germany 6459\n", + "United Kingdom 6221\n", + "Canada 3393\n", + "Name: count, dtype: int64 \n", + "\n", + "Column Student has 3 unique values.\n", + "Student\n", + "No 70399\n", + "Yes, full-time 18394\n", + "Yes, part-time 6108\n", + "Name: count, dtype: int64 \n", + "\n", + "Column Employment has 6 unique values.\n", + "Employment\n", + "Employed full-time 70495\n", + "Independent contractor, freelancer, or self-employed 9282\n", + "Not employed, but looking for work 5805\n", + "Employed part-time 5380\n", + "Not employed, and not looking for work 4132\n", + "Name: count, dtype: int64 \n", + "\n", + "Column FormalEducation has 9 unique values.\n", + "FormalEducation\n", + "Bachelor’s degree (BA, BS, B.Eng., etc.) 43659\n", + "Master’s degree (MA, MS, M.Eng., MBA, etc.) 21396\n", + "Some college/university study without earning a degree 11710\n", + "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.) 8951\n", + "Associate degree 2970\n", + "Name: count, dtype: int64 \n", + "\n", + "Column UndergradMajor has 12 unique values.\n", + "UndergradMajor\n", + "Computer science, computer engineering, or software engineering 50336\n", + "Another engineering discipline (ex. civil, electrical, mechanical) 6945\n", + "Information systems, information technology, or system administration 6507\n", + "A natural science (ex. biology, chemistry, physics) 3050\n", + "Mathematics or statistics 2818\n", + "Name: count, dtype: int64 \n", + "\n", + "Column CompanySize has 8 unique values.\n", + "CompanySize\n", + "20 to 99 employees 16996\n", + "100 to 499 employees 14011\n", + "10,000 or more employees 9757\n", + "10 to 19 employees 8007\n", + "1,000 to 4,999 employees 7634\n", + "Name: count, dtype: int64 \n", + "\n", + "Column DevType has 9568 unique values.\n", + "DevType\n", + "Back-end developer 6417\n", + "Full-stack developer 6104\n", + "Back-end developer;Front-end developer;Full-stack developer 4460\n", + "Mobile developer 3518\n", + "Student 3222\n", + "Name: count, dtype: int64 \n", + "\n", + "Column YearsCoding has 11 unique values.\n", + "YearsCoding\n", + "3-5 years 23313\n", + "6-8 years 19338\n", + "9-11 years 12169\n", + "0-2 years 10682\n", + "12-14 years 8030\n", + "Name: count, dtype: int64 \n", + "\n", + "Column YearsCodingProf has 11 unique values.\n", + "YearsCodingProf\n", + "0-2 years 23421\n", + "3-5 years 21362\n", + "6-8 years 11385\n", + "9-11 years 7573\n", + "12-14 years 4287\n", + "Name: count, dtype: int64 \n", + "\n", + "Column JobSatisfaction has 7 unique values.\n", + "JobSatisfaction\n", + "Moderately satisfied 26005\n", + "Extremely satisfied 12436\n", + "Slightly satisfied 10012\n", + "Slightly dissatisfied 7057\n", + "Moderately dissatisfied 6318\n", + "Name: count, dtype: int64 \n", + "\n", + "Column CareerSatisfaction has 7 unique values.\n", + "CareerSatisfaction\n", + "Moderately satisfied 27926\n", + "Extremely satisfied 14316\n", + "Slightly satisfied 13484\n", + "Slightly dissatisfied 6587\n", + "Neither satisfied nor dissatisfied 6316\n", + "Name: count, dtype: int64 \n", + "\n", + "Column HopeFiveYears has 7 unique values.\n", + "HopeFiveYears\n", + "Working in a different or more specialized technical role than the one I'm in now 25643\n", + "Working as a founder or co-founder of my own company 19444\n", + "Doing the same work 14724\n", + "Working as an engineering manager or other functional manager 7483\n", + "Working as a product manager or project manager 5004\n", + "Name: count, dtype: int64 \n", + "\n", + "Column JobSearchStatus has 3 unique values.\n", + "JobSearchStatus\n", + "I’m not actively looking, but I am open to new opportunities 47556\n", + "I am not interested in new job opportunities 19296\n", + "I am actively looking for a job 12636\n", + "Name: count, dtype: int64 \n", + "\n", + "Column LastNewJob has 5 unique values.\n", + "LastNewJob\n", + "Less than a year ago 27321\n", + "Between 1 and 2 years ago 17332\n", + "More than 4 years ago 14871\n", + "Between 2 and 4 years ago 14792\n", + "I've never had a job 4573\n", + "Name: count, dtype: int64 \n", + "\n", + "Column UpdateCV has 8 unique values.\n", + "UpdateCV\n", + "My job status or other personal status changed 27344\n", + "A recruiter contacted me 9071\n", + "I had a negative experience or interaction at work 7339\n", + "A friend told me about a job opportunity 6997\n", + "I saw an employer’s advertisement 6756\n", + "Name: count, dtype: int64 \n", + "\n", + "Column Currency has 19 unique values.\n", + "Currency\n", + "U.S. dollars ($) 20601\n", + "Euros (€) 15201\n", + "Indian rupees (₹) 7908\n", + "British pounds sterling (£) 4856\n", + "Canadian dollars (C$) 2535\n", + "Name: count, dtype: int64 \n", + "\n", + "Column Salary has 3998 unique values.\n", + "Salary\n", + "0 1121\n", + "60000 1095\n", + "100000 1086\n", + "50000 1071\n", + "80000 888\n", + "Name: count, dtype: int64 \n", + "\n", + "Column SalaryType has 3 unique values.\n", + "SalaryType\n", + "Monthly 26252\n", + "Yearly 22556\n", + "Weekly 2262\n", + "Name: count, dtype: int64 \n", + "\n", + "Column CurrencySymbol has 112 unique values.\n", + "CurrencySymbol\n", + "USD 17330\n", + "EUR 12781\n", + "INR 6633\n", + "GBP 4196\n", + "CAD 2122\n", + "Name: count, dtype: int64 \n", + "\n", + "Column CommunicationTools has 1149 unique values.\n", + "CommunicationTools\n", + "Office / productivity suite (Microsoft Office, Google Suite, etc.) 3523\n", + "Slack 3391\n", + "Confluence;Jira;Slack 2256\n", + "Other chat system (IRC, proprietary software, etc.) 1444\n", + "Jira;Slack 1402\n", + "Name: count, dtype: int64 \n", + "\n", + "Column TimeFullyProductive has 6 unique values.\n", + "TimeFullyProductive\n", + "One to three months 23163\n", + "Less than a month 15579\n", + "Three to six months 9026\n", + "Six to nine months 2625\n", + "Nine months to a year 875\n", + "Name: count, dtype: int64 \n", + "\n", + "Column EducationTypes has 494 unique values.\n", + "EducationTypes\n", + "Taught yourself a new language, framework, or tool without taking a formal course 6900\n", + "Taken an online course in programming or software development (e.g. a MOOC);Taught yourself a new language, framework, or tool without taking a formal course 4509\n", + "Taught yourself a new language, framework, or tool without taking a formal course;Contributed to open source software 4082\n", + "Received on-the-job training in software development;Taught yourself a new language, framework, or tool without taking a formal course 2375\n", + "Taken an online course in programming or software development (e.g. a MOOC);Taught yourself a new language, framework, or tool without taking a formal course;Contributed to open source software 2205\n", + "Name: count, dtype: int64 \n", + "\n", + "Column SelfTaughtTypes has 471 unique values.\n", + "SelfTaughtTypes\n", + "The official documentation and/or standards for the technology;Questions & answers on Stack Overflow 3426\n", + "The official documentation and/or standards for the technology;Questions & answers on Stack Overflow;The technology’s online help system 3123\n", + "The official documentation and/or standards for the technology;Questions & answers on Stack Overflow;Online developer communities other than Stack Overflow (ex. forums, listservs, IRC channels, etc.);The technology’s online help system 3054\n", + "The official documentation and/or standards for the technology;Questions & answers on Stack Overflow;Online developer communities other than Stack Overflow (ex. forums, listservs, IRC channels, etc.) 3010\n", + "The official documentation and/or standards for the technology;A book or e-book from O’Reilly, Apress, or a similar publisher;Questions & answers on Stack Overflow 2897\n", + "Name: count, dtype: int64 \n", + "\n", + "Column TimeAfterBootcamp has 8 unique values.\n", + "TimeAfterBootcamp\n", + "I already had a full-time job as a developer when I began the program 3025\n", + "Immediately after graduating 1085\n", + "One to three months 668\n", + "I haven’t gotten a developer job 581\n", + "Less than a month 496\n", + "Name: count, dtype: int64 \n", + "\n", + "Column HackathonReasons has 127 unique values.\n", + "HackathonReasons\n", + "To improve my general technical skills or programming ability;To improve my knowledge of a specific programming language, framework, or other technology;Because I find it enjoyable 3160\n", + "Because I find it enjoyable 3048\n", + "To improve my general technical skills or programming ability;Because I find it enjoyable 1902\n", + "To improve my general technical skills or programming ability;To improve my knowledge of a specific programming language, framework, or other technology;To improve my ability to work on a team with other programmers;Because I find it enjoyable 1061\n", + "To improve my general technical skills or programming ability 875\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AgreeDisagree1 has 5 unique values.\n", + "AgreeDisagree1\n", + "Agree 36777\n", + "Neither Agree nor Disagree 14676\n", + "Strongly agree 11332\n", + "Disagree 3941\n", + "Strongly disagree 1491\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AgreeDisagree2 has 5 unique values.\n", + "AgreeDisagree2\n", + "Agree 18673\n", + "Neither Agree nor Disagree 17995\n", + "Disagree 17523\n", + "Strongly disagree 8826\n", + "Strongly agree 5329\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AgreeDisagree3 has 5 unique values.\n", + "AgreeDisagree3\n", + "Disagree 23341\n", + "Neither Agree nor Disagree 17133\n", + "Strongly disagree 15361\n", + "Agree 9877\n", + "Strongly agree 2652\n", + "Name: count, dtype: int64 \n", + "\n", + "Column LanguageWorkedWith has 26678 unique values.\n", + "LanguageWorkedWith\n", + "C#;JavaScript;SQL;HTML;CSS 1347\n", + "JavaScript;PHP;SQL;HTML;CSS 1235\n", + "Java 1030\n", + "JavaScript;HTML;CSS 881\n", + "C#;JavaScript;SQL;TypeScript;HTML;CSS 828\n", + "Name: count, dtype: int64 \n", + "\n", + "Column LanguageDesireNextYear has 28657 unique values.\n", + "LanguageDesireNextYear\n", + "Python 908\n", + "Java 608\n", + "C#;JavaScript;SQL;TypeScript;HTML;CSS 605\n", + "C# 594\n", + "JavaScript;HTML;CSS 550\n", + "Name: count, dtype: int64 \n", + "\n", + "Column DatabaseWorkedWith has 6877 unique values.\n", + "DatabaseWorkedWith\n", + "MySQL 5968\n", + "SQL Server 5090\n", + "SQL Server;MySQL 3017\n", + "PostgreSQL 2091\n", + "MySQL;PostgreSQL 1400\n", + "Name: count, dtype: int64 \n", + "\n", + "Column DatabaseDesireNextYear has 10028 unique values.\n", + "DatabaseDesireNextYear\n", + "MySQL 2889\n", + "PostgreSQL 2564\n", + "SQL Server 2413\n", + "MongoDB 2091\n", + "SQL Server;MySQL 1125\n", + "Name: count, dtype: int64 \n", + "\n", + "Column PlatformWorkedWith has 9320 unique values.\n", + "PlatformWorkedWith\n", + "Windows Desktop or Server 5419\n", + "Linux 4703\n", + "Linux;Windows Desktop or Server 2527\n", + "Android 1660\n", + "AWS 1393\n", + "Name: count, dtype: int64 \n", + "\n", + "Column PlatformDesireNextYear has 13704 unique values.\n", + "PlatformDesireNextYear\n", + "Linux 3793\n", + "Windows Desktop or Server 2139\n", + "Linux;Windows Desktop or Server 1354\n", + "Android 1271\n", + "AWS 1172\n", + "Name: count, dtype: int64 \n", + "\n", + "Column FrameworkWorkedWith has 1014 unique values.\n", + "FrameworkWorkedWith\n", + ".NET Core 4517\n", + "Node.js 4453\n", + "Node.js;React 3436\n", + "Spring 2830\n", + "Angular;Node.js 2594\n", + "Name: count, dtype: int64 \n", + "\n", + "Column FrameworkDesireNextYear has 1786 unique values.\n", + "FrameworkDesireNextYear\n", + "Node.js;React 3797\n", + ".NET Core 2924\n", + "Node.js 2797\n", + "React 2249\n", + "TensorFlow 2204\n", + "Name: count, dtype: int64 \n", + "\n", + "Column IDE has 7884 unique values.\n", + "IDE\n", + "Visual Studio;Visual Studio Code 2548\n", + "Notepad++;Visual Studio;Visual Studio Code 2447\n", + "Notepad++;Visual Studio 2364\n", + "Visual Studio 2063\n", + "Vim 2017\n", + "Name: count, dtype: int64 \n", + "\n", + "Column OperatingSystem has 4 unique values.\n", + "OperatingSystem\n", + "Windows 38022\n", + "MacOS 20325\n", + "Linux-based 17684\n", + "BSD/Unix 148\n", + "Name: count, dtype: int64 \n", + "\n", + "Column NumberMonitors has 5 unique values.\n", + "NumberMonitors\n", + "2 39063\n", + "1 24362\n", + "3 10984\n", + "More than 4 1085\n", + "4 904\n", + "Name: count, dtype: int64 \n", + "\n", + "Column Methodology has 480 unique values.\n", + "Methodology\n", + "Agile;Scrum 9724\n", + "Agile 8632\n", + "Agile;Kanban;Scrum 5351\n", + "Agile;Kanban;Pair programming;Scrum 2476\n", + "Agile;Pair programming;Scrum 2461\n", + "Name: count, dtype: int64 \n", + "\n", + "Column VersionControl has 90 unique values.\n", + "VersionControl\n", + "Git 44133\n", + "Git;Subversion 6789\n", + "Git;Team Foundation Version Control 4208\n", + "I don't use version control 2988\n", + "Subversion 1962\n", + "Name: count, dtype: int64 \n", + "\n", + "Column CheckInCode has 6 unique values.\n", + "CheckInCode\n", + "Multiple times per day 43566\n", + "A few times per week 13808\n", + "Once a day 6587\n", + "Weekly or a few times per month 4983\n", + "Less than once per month 2337\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AdBlocker has 3 unique values.\n", + "AdBlocker\n", + "Yes 55011\n", + "No 17144\n", + "I'm not sure/I don't know 3900\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AdBlockerDisable has 3 unique values.\n", + "AdBlockerDisable\n", + "Yes 38798\n", + "No 13210\n", + "I'm not sure/I can't remember 2890\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AdBlockerReasons has 63 unique values.\n", + "AdBlockerReasons\n", + "The website I was visiting forced me to disable it to access their content 7410\n", + "The ad-blocking software was causing display issues on a website 3843\n", + "I wanted to support the website I was visiting by viewing their ads 3583\n", + "The website I was visiting forced me to disable it to access their content;The ad-blocking software was causing display issues on a website 2976\n", + "The website I was visiting forced me to disable it to access their content;The website I was visiting asked me to disable it;I wanted to support the website I was visiting by viewing their ads 2372\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AdsAgreeDisagree1 has 5 unique values.\n", + "AdsAgreeDisagree1\n", + "Somewhat agree 29875\n", + "Neither agree nor disagree 13693\n", + "Strongly agree 11657\n", + "Somewhat disagree 10464\n", + "Strongly disagree 8796\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AdsAgreeDisagree2 has 5 unique values.\n", + "AdsAgreeDisagree2\n", + "Somewhat agree 27589\n", + "Neither agree nor disagree 17665\n", + "Strongly agree 12078\n", + "Somewhat disagree 9960\n", + "Strongly disagree 7121\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AdsAgreeDisagree3 has 5 unique values.\n", + "AdsAgreeDisagree3\n", + "Neither agree nor disagree 22096\n", + "Somewhat agree 16738\n", + "Somewhat disagree 14644\n", + "Strongly agree 14056\n", + "Strongly disagree 6880\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AdsActions has 15 unique values.\n", + "AdsActions\n", + "Stopped going to a website because of their advertising 16992\n", + "Saw an online advertisement and then researched it (without clicking on the ad) 7393\n", + "Clicked on an online advertisement;Saw an online advertisement and then researched it (without clicking on the ad) 6422\n", + "Clicked on an online advertisement 6248\n", + "Saw an online advertisement and then researched it (without clicking on the ad);Stopped going to a website because of their advertising 6239\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AIDangerous has 4 unique values.\n", + "AIDangerous\n", + "Algorithms making important decisions 18020\n", + "Artificial intelligence surpassing human intelligence (\"the singularity\") 17645\n", + "Evolving definitions of \"fairness\" in algorithmic versus human decisions 14958\n", + "Increasing automation of jobs 12492\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AIInteresting has 4 unique values.\n", + "AIInteresting\n", + "Increasing automation of jobs 26644\n", + "Algorithms making important decisions 15379\n", + "Artificial intelligence surpassing human intelligence (\"the singularity\") 15231\n", + "Evolving definitions of \"fairness\" in algorithmic versus human decisions 8113\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AIResponsible has 4 unique values.\n", + "AIResponsible\n", + "The developers or the people creating the AI 31348\n", + "A governmental or other regulatory body 18263\n", + "Prominent industry leaders 10885\n", + "Nobody 5057\n", + "Name: count, dtype: int64 \n", + "\n", + "Column AIFuture has 3 unique values.\n", + "AIFuture\n", + "I'm excited about the possibilities more than worried about the dangers. 50773\n", + "I'm worried about the dangers more than I'm excited about the possibilities. 13269\n", + "I don't care about it, or I haven't thought about it. 5686\n", + "Name: count, dtype: int64 \n", + "\n", + "Column EthicsChoice has 3 unique values.\n", + "EthicsChoice\n", + "No 41441\n", + "Depends on what it is 25931\n", + "Yes 3410\n", + "Name: count, dtype: int64 \n", + "\n", + "Column EthicsReport has 4 unique values.\n", + "EthicsReport\n", + "Depends on what it is 32812\n", + "Yes, but only within the company 25160\n", + "Yes, and publicly 9200\n", + "No 3254\n", + "Name: count, dtype: int64 \n", + "\n", + "Column EthicsResponsible has 3 unique values.\n", + "EthicsResponsible\n", + "Upper management at the company/organization 37118\n", + "The person who came up with the idea 14726\n", + "The developer who wrote it 12696\n", + "Name: count, dtype: int64 \n", + "\n", + "Column EthicalImplications has 3 unique values.\n", + "EthicalImplications\n", + "Yes 55200\n", + "Unsure / I don't know 9893\n", + "No 4216\n", + "Name: count, dtype: int64 \n", + "\n", + "Column StackOverflowRecommend has 11 unique values.\n", + "StackOverflowRecommend\n", + "10 (Very Likely) 52790\n", + "9 8450\n", + "8 7667\n", + "7 3887\n", + "6 1320\n", + "Name: count, dtype: int64 \n", + "\n", + "Column StackOverflowVisit has 6 unique values.\n", + "StackOverflowVisit\n", + "Daily or almost daily 24964\n", + "Multiple times per day 23864\n", + "A few times per week 17179\n", + "A few times per month or weekly 8819\n", + "Less than once per month or monthly 1564\n", + "Name: count, dtype: int64 \n", + "\n", + "Column StackOverflowHasAccount has 3 unique values.\n", + "StackOverflowHasAccount\n", + "Yes 67146\n", + "No 6725\n", + "I'm not sure / I can't remember 2920\n", + "Name: count, dtype: int64 \n", + "\n", + "Column StackOverflowParticipate has 6 unique values.\n", + "StackOverflowParticipate\n", + "Less than once per month or monthly 25778\n", + "A few times per month or weekly 14866\n", + "I have never participated in Q&A on Stack Overflow 11393\n", + "A few times per week 7665\n", + "Daily or almost daily 3908\n", + "Name: count, dtype: int64 \n", + "\n", + "Column StackOverflowJobs has 3 unique values.\n", + "StackOverflowJobs\n", + "Yes 38727\n", + "No, I knew that Stack Overflow had a jobs board but have never used or visited it 26968\n", + "No, I didn't know that Stack Overflow had a jobs board 9602\n", + "Name: count, dtype: int64 \n", + "\n", + "Column StackOverflowDevStory has 4 unique values.\n", + "StackOverflowDevStory\n", + "No, and I don't know what that is 24019\n", + "No, I know what it is but I don't have one 15564\n", + "No, I have one but it's out of date 15128\n", + "Yes 10966\n", + "Name: count, dtype: int64 \n", + "\n", + "Column StackOverflowJobsRecommend has 11 unique values.\n", + "StackOverflowJobsRecommend\n", + "10 (Very Likely) 10137\n", + "5 5963\n", + "7 5429\n", + "8 5194\n", + "6 3782\n", + "Name: count, dtype: int64 \n", + "\n", + "Column StackOverflowConsiderMember has 3 unique values.\n", + "StackOverflowConsiderMember\n", + "Yes 42146\n", + "No 17019\n", + "I'm not sure 16842\n", + "Name: count, dtype: int64 \n", + "\n", + "Column HypotheticalTools1 has 5 unique values.\n", + "HypotheticalTools1\n", + "Somewhat interested 21315\n", + "Not at all interested 14229\n", + "A little bit interested 13617\n", + "Very interested 13345\n", + "Extremely interested 7562\n", + "Name: count, dtype: int64 \n", + "\n", + "Column HypotheticalTools2 has 5 unique values.\n", + "HypotheticalTools2\n", + "Not at all interested 20278\n", + "Somewhat interested 15770\n", + "A little bit interested 13374\n", + "Very interested 13084\n", + "Extremely interested 7546\n", + "Name: count, dtype: int64 \n", + "\n", + "Column HypotheticalTools3 has 5 unique values.\n", + "HypotheticalTools3\n", + "Somewhat interested 18144\n", + "Very interested 17127\n", + "Not at all interested 12896\n", + "A little bit interested 12554\n", + "Extremely interested 9278\n", + "Name: count, dtype: int64 \n", + "\n", + "Column HypotheticalTools4 has 5 unique values.\n", + "HypotheticalTools4\n", + "Very interested 19133\n", + "Somewhat interested 18292\n", + "Extremely interested 12342\n", + "A little bit interested 10752\n", + "Not at all interested 9467\n", + "Name: count, dtype: int64 \n", + "\n", + "Column HypotheticalTools5 has 5 unique values.\n", + "HypotheticalTools5\n", + "Somewhat interested 18004\n", + "Very interested 17967\n", + "Extremely interested 12913\n", + "A little bit interested 11598\n", + "Not at all interested 9563\n", + "Name: count, dtype: int64 \n", + "\n", + "Column WakeTime has 11 unique values.\n", + "WakeTime\n", + "Between 7:01 - 8:00 AM 21250\n", + "Between 6:01 - 7:00 AM 20322\n", + "Between 8:01 - 9:00 AM 10592\n", + "Between 5:00 - 6:00 AM 8922\n", + "I do not have a set schedule 3842\n", + "Name: count, dtype: int64 \n", + "\n", + "Column HoursComputer has 5 unique values.\n", + "HoursComputer\n", + "9 - 12 hours 37983\n", + "5 - 8 hours 22070\n", + "Over 12 hours 9549\n", + "1 - 4 hours 2349\n", + "Less than 1 hour 182\n", + "Name: count, dtype: int64 \n", + "\n", + "Column HoursOutside has 5 unique values.\n", + "HoursOutside\n", + "1 - 2 hours 27788\n", + "30 - 59 minutes 24002\n", + "Less than 30 minutes 11223\n", + "3 - 4 hours 7186\n", + "Over 4 hours 1825\n", + "Name: count, dtype: int64 \n", + "\n", + "Column SkipMeals has 4 unique values.\n", + "SkipMeals\n", + "Never 45772\n", + "1 - 2 times per week 18164\n", + "3 - 4 times per week 4303\n", + "Daily or almost every day 3707\n", + "Name: count, dtype: int64 \n", + "\n", + "Column ErgonomicDevices has 15 unique values.\n", + "ErgonomicDevices\n", + "Ergonomic keyboard or mouse 10164\n", + "Standing desk 9935\n", + "Wrist/hand supports or braces 3276\n", + "Standing desk;Ergonomic keyboard or mouse 3047\n", + "Ergonomic keyboard or mouse;Wrist/hand supports or braces 2006\n", + "Name: count, dtype: int64 \n", + "\n", + "Column Exercise has 4 unique values.\n", + "Exercise\n", + "I don't typically exercise 26995\n", + "1 - 2 times per week 20932\n", + "3 - 4 times per week 14318\n", + "Daily or almost every day 9863\n", + "Name: count, dtype: int64 \n", + "\n", + "Column Gender has 15 unique values.\n", + "Gender\n", + "Male 59458\n", + "Female 4025\n", + "Non-binary, genderqueer, or gender non-conforming 284\n", + "Female;Transgender 145\n", + "Male;Non-binary, genderqueer, or gender non-conforming 128\n", + "Name: count, dtype: int64 \n", + "\n", + "Column SexualOrientation has 14 unique values.\n", + "SexualOrientation\n", + "Straight or heterosexual 55013\n", + "Bisexual or Queer 1950\n", + "Gay or Lesbian 1181\n", + "Asexual 717\n", + "Straight or heterosexual;Bisexual or Queer 351\n", + "Name: count, dtype: int64 \n", + "\n", + "Column EducationParents has 9 unique values.\n", + "EducationParents\n", + "Bachelor’s degree (BA, BS, B.Eng., etc.) 18090\n", + "Master’s degree (MA, MS, M.Eng., MBA, etc.) 13630\n", + "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.) 10664\n", + "Some college/university study without earning a degree 5715\n", + "Other doctoral degree (Ph.D, Ed.D., etc.) 3710\n", + "Name: count, dtype: int64 \n", + "\n", + "Column RaceEthnicity has 71 unique values.\n", + "RaceEthnicity\n", + "White or of European descent 40541\n", + "South Asian 6213\n", + "Hispanic or Latino/Latina 2718\n", + "East Asian 2350\n", + "Middle Eastern 1774\n", + "Name: count, dtype: int64 \n", + "\n", + "Column Age has 7 unique values.\n", + "Age\n", + "25 - 34 years old 31759\n", + "18 - 24 years old 15249\n", + "35 - 44 years old 11477\n", + "45 - 54 years old 3313\n", + "Under 18 years old 1638\n", + "Name: count, dtype: int64 \n", + "\n", + "Column Dependents has 2 unique values.\n", + "Dependents\n", + "No 44478\n", + "Yes 18118\n", + "Name: count, dtype: int64 \n", + "\n", + "Column MilitaryUS has 2 unique values.\n", + "MilitaryUS\n", + "No 15043\n", + "Yes 738\n", + "Name: count, dtype: int64 \n", + "\n", + "Column SurveyTooLong has 3 unique values.\n", + "SurveyTooLong\n", + "The survey was an appropriate length 33257\n", + "The survey was too long 32214\n", + "The survey was too short 470\n", + "Name: count, dtype: int64 \n", + "\n", + "Column SurveyEasy has 5 unique values.\n", + "SurveyEasy\n", + "Somewhat easy 24434\n", + "Very easy 21503\n", + "Neither easy nor difficult 15300\n", + "Somewhat difficult 4165\n", + "Very difficult 477\n", + "Name: count, dtype: int64 \n", + "\n" + ] + }, + { + "data": { + "image/png": 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U6TGlpaVRWlpa488BAADYcdX6K04fdcghh8QLL7wQHTt2jL333rvCrVmzZhHxz135evXqFZdccknMnTs3GjduHPfdd18tzxwAANhR1blwGjZsWLz11ltx1llnxaxZs+KVV16JKVOmxJAhQ2LDhg0xc+bMuPLKK+O5556L1157LSZPnhx/+9vfYt99963tqQMAADuoOvFWvX9VXl4eTz31VIwaNSqOP/74WLduXXTo0CFOOOGEaNCgQbRo0SKeeOKJ+PGPfxxr1qyJDh06xA9/+MM48cQTa3vqAADADqrWd9Xb1jbtnGFXPQAA2LbsqgcAALADE04AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkFFSlQc99thj0adPn2qeyrb18LWjokWLFrU9DQAAYDtQpVecTjjhhOjSpUtcfvnlsXTp0uqeEwAAQJ1SpXB64403YuTIkTF58uTo1KlTHH/88XHXXXfF+vXrq3t+AAAAta5K4bTbbrvFiBEjYs6cOfHcc8/FPvvsE8OGDYu2bdvGiBEj4o9//GN1zxMAAKDWfOrNIQ466KD43ve+F8OGDYv3338//vu//zt69uwZRx11VLzwwgvVMUcAAIBaVeVw+uCDD+Kee+6Jfv36RYcOHeLhhx+OSZMmxYoVK2Lx4sXRrl27OP3006tzrgAAALWiSrvqXXDBBfHrX/86IiIGDBgQ11xzTXTv3r14vlmzZnH11VdHx44dq2WSAAAAtalK4fTiiy/GxIkT48tf/nI0btx4s2PKy8tj+vTpn2pyAAAAdUEhpZRqexLb0po1a6KsrCxWr17te5wAAKAe25o2qNIrThERixYtiokTJ8bChQujUChEt27d4oILLoh99tmnqpcEAACok6q0OcQ999wT3bt3j9mzZ8eBBx4YBxxwQMyZMye6d+8ed999d3XPEQAAoFZV6a16nTt3jgEDBsSll15a4fi4cePil7/8ZbzyyivVNsHq5q16AABAxNa1QZVecVq+fHmcc845lY4PGDAgli9fXpVLAgAA1FlVCqc+ffrEH/7wh0rHn3zyyTjqqKM+9aQAAADqki3eHOKBBx4o/vmUU06JUaNGxezZs+Owww6LiIhnnnkm7r777rjkkkuqf5YAAAC1aIs/49SgwZa9OFUoFGLDhg2falI1yWecAACAiBrajnzjxo2femIAAADboyp9xgkAAKA+qXI4PfLII3HyySdHly5dYu+9946TTz45pk2bVp1zAwAAqBOqFE6TJk2KE044IZo3bx4jR46MESNGRIsWLaJfv34xadKk6p4jAABArarSF+DuueeeMXr06Bg+fHiF49dff31cccUV8cYbb1TbBKubzSEAAICIbfAFuGvWrIkTTjih0vEvfvGLsWbNmqpcEgAAoM6qUjidcsopcd9991U6/tvf/jb69+//qScFAABQl2zxduTXXXdd8c/77rtvXHHFFfHYY4/F4YcfHhH//ALcp556Ki666KLqnyUAAEAt2uLPOHXq1GnLLlgoxCuvvPKpJlWTfMYJAACIqKEvwF28ePGnnhgAAMD26FN/AW5KKaqwMR8AAMB2o8rhdNttt0WPHj2iadOm0bRp0zjggAPil7/8ZXXODQAAoE7Y4rfq/asf/ehHMXbs2Bg+fHj06tUrUkrx1FNPxXnnnRdvvvlmfOtb36rueQIAANSaKn0BbqdOneKSSy6Jc845p8LxX/ziFzF+/Pg6/Xkom0MAAAAR2+ALcJctWxZHHHFEpeNHHHFELFu2rCqXBAAAqLOqFE5777133HXXXZWO/+Y3v4nPfOYzn3pSAAAAdUmVPuN0ySWXxBlnnBFPPPFE9OrVKwqFQjz55JPxyCOPbDaoAAAAtmdVesXpy1/+csycOTNatWoV999/f0yePDlatWoVs2bNilNPPbW65wgAAFCrtmpziDVr1mzRuLq86YLNIQAAgIita4OteqveLrvsEoVCITtuw4YNW3NZAACAOm2rwmn69OnFP6eUol+/fvHTn/409txzz2qfGAAAQF2xVeHUu3fvCvcbNmwYhx12WHTu3LlaJwUAAFCXVGlXvR3Bl+4ZHyU7ldb2NGCrTTnzqtqeAgBAvVOlXfUAAADqk08dTluyWQQAAMD2bKveqnfaaadVuL927do477zzolmzZhWOT548+dPPDAAAoI7YqnAqKyurcH/AgAHVOhkAAIC6aKvC6dZbb62peQAAANRZNocAAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGQIJwAAgAzhBAAAkCGcAAAAMoQTAABAhnACAADIEE4AAAAZwgkAACBDOAEAAGTUmXAaPHhwFAqFuPrqqyscv//++6NQKNTSrAAAAOpQOEVENGnSJCZMmBBvv/12bU8FAACgqE6F07HHHhtt2rSJq6666mPH3HvvvbH//vtHaWlpdOzYMX74wx9+4jXXrVsXa9asqXADAADYGnUqnBo2bBhXXnllTJw4MV5//fVK52fPnh1f/epX48wzz4z58+fH+PHjY+zYsfHzn//8Y6951VVXRVlZWfHWrl27GnwGAADAjqhOhVNExKmnnhoHHXRQjBs3rtK5H/3oR9G3b98YO3ZsdO3aNQYPHhzDhw+P//zP//zY640ePTpWr15dvC1durQmpw8AAOyA6lw4RURMmDAhfvGLX8SLL75Y4fjChQujV69eFY716tUrXn755diwYcNmr1VaWhotWrSocAMAANgadTKcvvCFL8Txxx8fF198cYXjKaVKO+yllLbl1AAAgHqopLYn8HGuvvrqOOigg6Jr167FY/vtt188+eSTFcY9/fTT0bVr12jYsOG2niIAAFBP1Nlw6tGjR5x99tkxceLE4rGLLrooPvvZz8Zll10WZ5xxRsyYMSMmTZoUN9xwQy3OFAAA2NHVybfqbXLZZZdVeCveIYccEnfddVfceeed0b179/jBD34Ql156aQwePLj2JgkAAOzwCqmefUhozZo1UVZWFkf/7FtRslNpbU8HttqUMz/+e84AANhym9pg9erV2U3k6vQrTgAAAHWBcAIAAMgQTgAAABnCCQAAIEM4AQAAZAgnAACADOEEAACQIZwAAAAyhBMAAECGcAIAAMgQTgAAABnCCQAAIEM4AQAAZAgnAACADOEEAACQIZwAAAAyhBMAAECGcAIAAMgQTgAAABnCCQAAIEM4AQAAZAgnAACADOEEAACQIZwAAAAyhBMAAECGcAIAAMgQTgAAABnCCQAAIEM4AQAAZAgnAACADOEEAACQIZwAAAAyhBMAAECGcAIAAMgQTgAAABnCCQAAIEM4AQAAZAgnAACADOEEAACQIZwAAAAyhBMAAECGcAIAAMgQTgAAABnCCQAAIEM4AQAAZAgnAACADOEEAACQIZwAAAAyhBMAAECGcAIAAMgQTgAAABnCCQAAIEM4AQAAZAgnAACADOEEAACQIZwAAAAyhBMAAECGcAIAAMgQTgAAABnCCQAAIEM4AQAAZAgnAACADOEEAACQIZwAAAAyhBMAAECGcAIAAMgQTgAAABnCCQAAIEM4AQAAZJTU9gRqy/1fGR8tWrSo7WkAAADbAa84AQAAZAgnAACADOEEAACQIZwAAAAyhBMAAECGcAIAAMgQTgAAABnCCQAAIEM4AQAAZAgnAACADOEEAACQIZwAAAAyhBMAAECGcAIAAMgQTgAAABnCCQAAIEM4AQAAZAgnAACAjJLansC2llKKiIg1a9bU8kwAAIDatKkJNjXCJ6l34bRq1aqIiGjXrl0tzwQAAKgL3n333SgrK/vEMfUunHbbbbeIiHjttdeyi0P1WLNmTbRr1y6WLl0aLVq0qO3p7PCs97Znzbct671tWe9tz5pvW9Z726pr651SinfffTfKy8uzY+tdODVo8M+PdZWVldWJv6z6pEWLFtZ8G7Le254137as97Zlvbc9a75tWe9tqy6t95a+mGJzCAAAgAzhBAAAkFHvwqm0tDTGjRsXpaWltT2VesOab1vWe9uz5tuW9d62rPe2Z823Leu9bW3P611IW7L3HgAAQD1W715xAgAA2FrCCQAAIEM4AQAAZAgnAACAjHoXTjfccEN06tQpmjRpEj179ow//OEPtT2lOueJJ56I/v37R3l5eRQKhbj//vsrnE8pxfjx46O8vDyaNm0affr0iRdeeKHCmHXr1sUFF1wQrVq1imbNmsUpp5wSr7/+eoUxb7/9dgwcODDKysqirKwsBg4cGO+8806FMa+99lr0798/mjVrFq1atYoRI0bE+vXra+Jp15qrrroqPvvZz0bz5s2jdevW8aUvfSkWLVpUYYw1rz433nhjHHDAAcUv3jv88MPjf//3f4vnrXXNuuqqq6JQKMSFF15YPGbNq9f48eOjUChUuLVp06Z43nrXjL/+9a8xYMCAaNmyZey0005x0EEHxezZs4vnrXv16dixY6Xf8UKhEMOGDYsIa13dPvzww/j+978fnTp1iqZNm0bnzp3j0ksvjY0bNxbH1Js1T/XInXfemRo1apRuueWW9OKLL6aRI0emZs2apSVLltT21OqU//mf/0ljxoxJ9957b4qIdN9991U4f/XVV6fmzZune++9N82fPz+dccYZqW3btmnNmjXFMeedd17ac88909SpU9OcOXPS0UcfnQ488MD04YcfFseccMIJqXv37unpp59OTz/9dOrevXs6+eSTi+c//PDD1L1793T00UenOXPmpKlTp6by8vI0fPjwGl+Dben4449Pt956a1qwYEGaN29eOumkk1L79u3Te++9VxxjzavPAw88kB588MG0aNGitGjRonTxxRenRo0apQULFqSUrHVNmjVrVurYsWM64IAD0siRI4vHrXn1GjduXNp///3TsmXLireVK1cWz1vv6vfWW2+lDh06pMGDB6eZM2emxYsXp2nTpqU///nPxTHWvfqsXLmywu/31KlTU0Sk6dOnp5SsdXW7/PLLU8uWLdPvf//7tHjx4nT33XennXfeOf34xz8ujqkva16vwulzn/tcOu+88yoc69atW/re975XSzOq+z4aThs3bkxt2rRJV199dfHY2rVrU1lZWfrJT36SUkrpnXfeSY0aNUp33nlnccxf//rX1KBBg/TQQw+llFJ68cUXU0SkZ555pjhmxowZKSLSSy+9lFL6Z8A1aNAg/fWvfy2O+fWvf51KS0vT6tWra+T51gUrV65MEZEef/zxlJI13xZ23XXX9NOf/tRa16B33303feYzn0lTp05NvXv3LoaTNa9+48aNSwceeOBmz1nvmjFq1Kh05JFHfux5616zRo4cmbp06ZI2btxorWvASSedlIYMGVLh2GmnnZYGDBiQUqpfv9/15q1669evj9mzZ8cXv/jFCse/+MUvxtNPP11Ls9r+LF68OJYvX15hHUtLS6N3797FdZw9e3Z88MEHFcaUl5dH9+7di2NmzJgRZWVl8fnPf7445rDDDouysrIKY7p37x7l5eXFMccff3ysW7euwtsfdjSrV6+OiIjddtstIqx5TdqwYUPceeed8f7778fhhx9urWvQsGHD4qSTTopjjz22wnFrXjNefvnlKC8vj06dOsWZZ54Zr7zySkRY75rywAMPxKGHHhqnn356tG7dOg4++OC45ZZbiuete81Zv3593H777TFkyJAoFArWugYceeSR8cgjj8Sf/vSniIj44x//GE8++WT069cvIurX73dJjf+EOuLNN9+MDRs2xB577FHh+B577BHLly+vpVltfzat1ebWccmSJcUxjRs3jl133bXSmE2PX758ebRu3brS9Vu3bl1hzEd/zq677hqNGzfeYf/OUkrx7W9/O4488sjo3r17RFjzmjB//vw4/PDDY+3atbHzzjvHfffdF/vtt1/xH2ZrXb3uvPPOmDNnTjz77LOVzvn9rn6f//zn47bbbouuXbvGihUr4vLLL48jjjgiXnjhBetdQ1555ZW48cYb49vf/nZcfPHFMWvWrBgxYkSUlpbGOeecY91r0P333x/vvPNODB48OCL8m1ITRo0aFatXr45u3bpFw4YNY8OGDXHFFVfEWWedFRH1a83rTThtUigUKtxPKVU6Rl5V1vGjYzY3vipjdiTDhw+P559/Pp588slK56x59dlnn31i3rx58c4778S9994bgwYNiscff7x43lpXn6VLl8bIkSNjypQp0aRJk48dZ82rz4knnlj8c48ePeLwww+PLl26xC9+8Ys47LDDIsJ6V7eNGzfGoYceGldeeWVERBx88MHxwgsvxI033hjnnHNOcZx1r34/+9nP4sQTT6zwCkSEta5Ov/nNb+L222+PO+64I/bff/+YN29eXHjhhVFeXh6DBg0qjqsPa15v3qrXqlWraNiwYaUaXblyZaVy5eNt2pnpk9axTZs2sX79+nj77bc/ccyKFSsqXf9vf/tbhTEf/Tlvv/12fPDBBzvk39kFF1wQDzzwQEyfPj322muv4nFrXv0aN24ce++9dxx66KFx1VVXxYEHHhjXXnutta4Bs2fPjpUrV0bPnj2jpKQkSkpK4vHHH4/rrrsuSkpKis/VmtecZs2aRY8ePeLll1/2O15D2rZtG/vtt1+FY/vuu2+89tprEeHf8ZqyZMmSmDZtWnz9618vHrPW1e873/lOfO9734szzzwzevToEQMHDoxvfetbcdVVV0VE/VrzehNOjRs3jp49e8bUqVMrHJ86dWocccQRtTSr7U+nTp2iTZs2FdZx/fr18fjjjxfXsWfPntGoUaMKY5YtWxYLFiwojjn88MNj9erVMWvWrOKYmTNnxurVqyuMWbBgQSxbtqw4ZsqUKVFaWho9e/as0ee5LaWUYvjw4TF58uR49NFHo1OnThXOW/Oal1KKdevWWesa0Ldv35g/f37MmzeveDv00EPj7LPPjnnz5kXnzp2teQ1bt25dLFy4MNq2bet3vIb06tWr0tdI/OlPf4oOHTpEhH/Ha8qtt94arVu3jpNOOql4zFpXv7///e/RoEHFZGjYsGFxO/J6teY1u/dE3bJpO/Kf/exn6cUXX0wXXnhhatasWXr11Vdre2p1yrvvvpvmzp2b5s6dmyIi/ehHP0pz584tbtt+9dVXp7KysjR58uQ0f/78dNZZZ212y8m99torTZs2Lc2ZMycdc8wxm91y8oADDkgzZsxIM2bMSD169NjslpN9+/ZNc+bMSdOmTUt77bXXDrfN5/nnn5/KysrSY489VmF71b///e/FMda8+owePTo98cQTafHixen5559PF198cWrQoEGaMmVKSslabwv/uqteSta8ul100UXpscceS6+88kp65pln0sknn5yaN29e/L/Oele/WbNmpZKSknTFFVekl19+Of3qV79KO+20U7r99tuLY6x79dqwYUNq3759GjVqVKVz1rp6DRo0KO25557F7cgnT56cWrVqlb773e8Wx9SXNa9X4ZRSStdff33q0KFDaty4cTrkkEOKWz7zf6ZPn54iotJt0KBBKaV/bjs5bty41KZNm1RaWpq+8IUvpPnz51e4xj/+8Y80fPjwtNtuu6WmTZumk08+Ob322msVxqxatSqdffbZqXnz5ql58+bp7LPPTm+//XaFMUuWLEknnXRSatq0adptt93S8OHD09q1a2vy6W9zm1vriEi33nprcYw1rz5Dhgwp/huw++67p759+xajKSVrvS18NJysefXa9P0pjRo1SuXl5em0005LL7zwQvG89a4Zv/vd71L37t1TaWlp6tatW7r55psrnLfu1evhhx9OEZEWLVpU6Zy1rl5r1qxJI0eOTO3bt09NmjRJnTt3TmPGjEnr1q0rjqkva15IKaWaf10LAABg+1VvPuMEAABQVcIJAAAgQzgBAABkCCcAAIAM4QQAAJAhnAAAADKEEwAAQIZwAgAAyBBOAAAAGcIJALbSq6++GoVCIebNm1fbUwFgGxFOAAAAGcIJgO3Oxo0bY8KECbH33ntHaWlptG/fPq644oqIiJg/f34cc8wx0bRp02jZsmV84xvfiPfee6/42D59+sSFF15Y4Xpf+tKXYvDgwcX7HTt2jCuvvDKGDBkSzZs3j/bt28fNN99cPN+pU6eIiDj44IOjUChEnz59auy5AlA3CCcAtjujR4+OCRMmxNixY+PFF1+MO+64I/bYY4/4+9//HieccELsuuuu8eyzz8bdd98d06ZNi+HDh2/1z/jhD38Yhx56aMydOzeGDh0a559/frz00ksRETFr1qyIiJg2bVosW7YsJk+eXK3PD4C6p6S2JwAAW+Pdd9+Na6+9NiZNmhSDBg2KiIguXbrEkUceGbfcckv84x//iNtuuy2aNWsWERGTJk2K/v37x4QJE2KPPfbY4p/Tr1+/GDp0aEREjBo1Kv7rv/4rHnvssejWrVvsvvvuERHRsmXLaNOmTTU/QwDqIq84AbBdWbhwYaxbty769u272XMHHnhgMZoiInr16hUbN26MRYsWbdXPOeCAA4p/LhQK0aZNm1i5cmXVJw7Adk04AbBdadq06ceeSylFoVDY7LlNxxs0aBAppQrnPvjgg0rjGzVqVOnxGzdu3NrpArCDEE4AbFc+85nPRNOmTeORRx6pdG6//faLefPmxfvvv1889tRTT0WDBg2ia9euERGx++67x7Jly4rnN2zYEAsWLNiqOTRu3Lj4WADqB+EEwHalSZMmMWrUqPjud78bt912W/zlL3+JZ555Jn72s5/F2WefHU2aNIlBgwbFggULYvr06XHBBRfEwIEDi59vOuaYY+LBBx+MBx98MF566aUYOnRovPPOO1s1h9atW0fTpk3joYceihUrVsTq1atr4JkCUJcIJwC2O2PHjo2LLroofvCDH8S+++4bZ5xxRqxcuTJ22mmnePjhh+Ott96Kz372s/GVr3wl+vbtG5MmTSo+dsiQITFo0KA455xzonfv3tGpU6c4+uijt+rnl5SUxHXXXRc33XRTlJeXx7/9279V91MEoI4ppI++0RsAAIAKvOIEAACQIZwAAAAyhBMAAECGcAIAAMgQTgAAABnCCQAAIEM4AQAAZAgnAACADOEEAACQIZwAAAAyhBMAAEDG/wN4X65Gz2MITAAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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REZF/xNdff0337t0pXLgw8+bNy/Cn6kREJPu05V5ERERERETECuln60RERERERESskAp6ERERERERESukgl5ERERERETECumleCJPgLS0NM6cOUPevHkxmUy5nY6IiIiIiOQSs9nMlStX8Pb2Jk+erNfgVdCLPAHOnDmDj49PbqchIiIiIiJPiJMnT1K0aNEsY1TQizwB8ubNC9z9H22+fPlyORsREREREcktycnJ+Pj4GDVCVlTQizwB7m2zz5cvnwp6ERERERF5qEdx9VI8ERERERERESukgl5ERERERETECmnLvcgT5MV6w7GzccjtNERERERE/jO+jpuV2ylkm1boRURERERERKyQCnoRERERERERK6SCXkRERERERMQKqaAXERERERERsUIq6EVERERERESskAp6ERERERERESukgl5ERERERETECqmgFxEREREREbFCKuhFRERERERErJAKehERERERERErpIJeRERERERExAqpoBcRERERERGxQiroRURERERERKyQCnqxOlu2bMFkMvHXX3/ldioiIiIiIiK5RgX9Y5o8eTI1atQgb968PPXUU7Rp04ZDhw5ZxJjNZiIiIvD29sbJyYkGDRpw8ODBLMcNCQmhTZs2OZLj999/T+3atcmbNy+FCxdmxIgR3L592yLms88+o0qVKjg7O1O8eHGmTZuWI+cWERERERGRf4YK+scUExNDv3792LlzJ9HR0dy+fZumTZty7do1I2bq1KnMmDGDOXPmsHv3bry8vGjSpAlXrlz5x/OLj4+nRYsWNGvWjH379rF8+XK+/PJL3njjDSPmm2++oXPnzvTt25eff/6ZefPmGfmKiIiIiIjIk0kF/WPasGEDISEhVKhQgcqVKxMZGcmJEyeIi4sD7q7Oz5w5k5EjR9K2bVsqVqxIVFQUKSkpfPrppxmOGRERQVRUFGvXrsVkMmEymdiyZQsABw4c4Nlnn8XJyYkCBQrw6quvcvXq1UzzW758OZUqVWLMmDGULl2a+vXrM3nyZObOnWvcUFiyZAlt2rShb9++lCxZkueee44RI0YwZcoUzGZzpmOfPn2aDh064OHhQYECBWjdujXHjx83jt/bZTBp0iQ8PT1xd3dn3Lhx3L59m2HDhpE/f36KFi3Kxx9/bPQ5fvw4JpOJ5cuXU7t2bRwdHalQoYIx/8ysWrWKChUq4ODggK+vL9OnTzeOjR8/noCAgHR9qlWrxpgxY4zvkZGR+Pn54ejoSPny5Zk3b94jzXfLli08/fTTuLi44O7uTp06dfjjjz+yzFtERERERCS7VNDnsMuXLwOQP39+AI4dO0ZSUhJNmzY1YhwcHKhfvz6xsbEZjjF06FDat29Ps2bNSExMJDExkdq1a5OSkkKzZs3w8PBg9+7drFy5ku+++47Q0NBM87l58yaOjo4WbU5OTty4ccO46ZBZzKlTpzItSFNSUmjYsCGurq5s3bqVbdu24erqSrNmzbh165YRt2nTJs6cOcPWrVuZMWMGERERtGzZEg8PD3bt2kXfvn3p27cvJ0+etBh/2LBhDBkyhH379lG7dm2ef/55Ll68mGEucXFxtG/fno4dO3LgwAEiIiIYPXo0ixYtAqBHjx788ssv7N692+gTHx/Pvn37CAkJAWDhwoWMHDmSt956i4SEBCZNmsTo0aOJiop6qPnevn2bNm3aUL9+feLj49mxYwevvvoqJpMp079LcnKyxUdERERERORRqKDPQWazmcGDB/PMM89QsWJFAJKSkgDw9PS0iPX09DSO3c/V1RUnJyccHBzw8vLCy8sLe3t7li5dyvXr11m8eDEVK1bk2WefZc6cOSxZsoSzZ89mOFZwcDCxsbEsW7aMO3fucPr0aSZOnAhAYmKiEfPFF1/w/fffk5aWxuHDh5k5c6ZFzP2WL19Onjx5+PDDDwkICMDPz8/YnfD31fT8+fMze/ZsypUrR48ePShXrhwpKSm8+eablClThvDwcOzt7dm+fbvF+KGhobRr1w4/Pz/mz5+Pm5sbH330UYa5zJgxg0aNGjF69GjKli1LSEgIoaGhxnsAihYtSnBwMJGRkUafyMhI6tevT8mSJQGYMGEC06dPp23btpQoUYK2bdsyaNAgPvjgg4eab3JyMpcvX6Zly5aUKlUKPz8/unXrRrFixTLMefLkybi5uRkfHx+fDONEREREREQyo4I+B4WGhhIfH8+yZcvSHbt/pdZsNme6epuZhIQEKleujIuLi9FWp04d0tLS0r2I756mTZsybdo0+vbti4ODA2XLluW5554DwMbGBoDevXsTGhpKy5Ytsbe3p1atWnTs2NEi5n5xcXH89ttv5M2bF1dXV1xdXcmfPz83btzg6NGjRlyFChXIk+d//5l5enpabH+3sbGhQIECnDt3zmL8oKAg49+2trZUr16dhISETK9LnTp1LNrq1KnDkSNHuHPnjjHHZcuWcePGDVJTU1m6dCk9evQA4Pz585w8eZKePXsac3F1dWXixInGXB403/z58xMSEkJwcDCtWrVi1qxZmd4MAQgPD+fy5cvG5/4dCiIiIiIiIg9im9sJ/Fv079+fL7/8kq1bt1K0aFGj3cvLC7i7Ul+4cGGj/dy5c+lW7R8kq5sAWd0cGDx4MIMGDSIxMREPDw+OHz9OeHg4JUqUMPpOmTKFSZMmkZSURKFChfj+++8B8PX1zXDMtLQ0qlWrxtKlS9MdK1SokPFvOzu7dHlm1JaWlpZp/g+aY0bX5f5n/1u1aoWDgwOrV6/GwcGBmzdv0q5dO2MucHfbfc2aNS363buh8TDzjYyMJCwsjA0bNrBixQpGjRpFdHQ0tWrVStfHwcEBBweHB85ZREREREQkMyroH5PZbKZ///6sXr2aLVu2GEXyPSVKlMDLy4vo6GgCAwMBuHXrFjExMUyZMiXTce3t7Y3V5Xv8/f2Jiori2rVrxir99u3byZMnD2XLls0yT5PJhLe3NwDLli3Dx8eHqlWrWsTY2NhQpEgRIyYoKIinnnoqw/GqVq3KihUreOqpp8iXL1+W586OnTt3Uq9ePQBu375NXFxcpu8K8Pf3Z9u2bRZtsbGxlC1b1ijIbW1t6datG5GRkTg4ONCxY0ecnZ2Bu7sGihQpwu+//07nzp0zPMfDzjcwMJDAwEDCw8MJCgri008/zbCgFxEREREReVzacv+Y+vXrxyeffMKnn35K3rx5SUpKIikpievXrwN3C+mBAwcyadIkVq9ezc8//0xISAjOzs506tQp03F9fX2Jj4/n0KFDXLhwgdTUVDp37oyjoyPdunXj559/ZvPmzfTv358uXbpkudo/bdo0Dhw4wMGDB5kwYQJvv/02s2fPNordCxcu8P777/Prr7+yf/9+BgwYwMqVK43n6DPSuXNnChYsSOvWrfnhhx84duwYMTExDBgwgFOnTmXvYv7N3LlzWb16Nb/++iv9+vXj0qVLxhb5+w0ZMoTvv/+eCRMmcPjwYaKiopgzZw5Dhw61iOvVqxebNm3im2++STdWREQEkydPZtasWRw+fJgDBw4QGRnJjBkzHmq+x44dIzw8nB07dvDHH3+wceNGDh8+jJ+f32NfCxERERERkYxohf4xzZ8/H4AGDRpYtEdGRhpvUB8+fDjXr1/n9ddf59KlS9SsWZONGzeSN2/eTMft3bs3W7ZsoXr16ly9epXNmzfToEEDvv32WwYMGECNGjVwdnamXbt2RtGZmW+++Ya33nqLmzdvUrlyZdauXUvz5s0tYqKiohg6dChms5mgoCDjJ9gy4+zszNatWxkxYgRt27blypUrFClShEaNGuXIiv3bb7/NlClT2LdvH6VKlWLt2rUULFgww9iqVavy2WefMWbMGCZMmEDhwoUZP368cf3vKVOmDLVr1+bixYvpttb36tULZ2dnpk2bxvDhw3FxcSEgIICBAwc+1HyvX7/Or7/+SlRUFBcvXqRw4cKEhobSp0+fx74WIiIiIiIiGTGZs/qhcZH/Y8ePH6dEiRLs27ePKlWq5OjYZrOZ8uXL06dPHwYPHpyjYz+u5ORk3NzcaFK5D3Y2erZeREREROT/ytdxs3I7BQv3aoPLly8/cLFUK/Tyn3Du3DmWLFnC6dOn6d69e26nIyIiIiIi8thU0Mt/gqenJwULFmTBggV4eHjkdjoiIiIiIiKPTQW9PFF8fX3T/eRcTtCTJSIiIiIi8m+jt9yLiIiIiIiIWCEV9CIiIiIiIiJWSAW9iIiIiIiIiBVSQS8iIiIiIiJihVTQi4iIiIiIiFghFfQiIiIiIiIiVkgFvYiIiIiIiIgVUkEvIiIiIiIiYoVU0IuIiIiIiIhYIdvcTkBE/ufzrVPJly9fbqchIiIiIiJWQCv0IiIiIiIiIlZIBb2IiIiIiIiIFVJBLyIiIiIiImKFVNCLiIiIiIiIWCEV9CIiIiIiIiJWSAW9iIiIiIiIiBVSQS8iIiIiIiJihVTQi4iIiIiIiFghFfQiIiIiIiIiVsg2txMQkf9p12kytnaOuZ2GiIiIyH/CN6vH5nYKIo9FK/QiIiIiIiIiVkgFvYiIiIiIiIgVUkEvIiIiIiIiYoVU0IuIiIiIiIhYIRX0IiIiIiIiIlZIBb2IiIiIiIiIFVJBLyIiIiIiImKFVNCLiIiIiIiIWCEV9CIiIiIiIiJWSAW9iIiIiIiIiBVSQS8iIiIiIiJihVTQi4iIiIiIiFghFfQiIiIiIiIiVkgFvVidiIgIqlSpkttpiIiIiIiI5Kr/bEG/detWWrVqhbe3NyaTiTVr1qSLMZvNRERE4O3tjZOTEw0aNODgwYMWMTdv3qR///4ULFgQFxcXnn/+eU6dOpXluRs0aMDAgQNzcDbw22+/kTdvXtzd3dMdmzt3Ln5+fjg5OVGuXDkWL15scTw1NZXx48dTqlQpHB0dqVy5Mhs2bMjR/ERERERERCRn/WcL+mvXrlG5cmXmzJmTaczUqVOZMWMGc+bMYffu3Xh5edGkSROuXLlixAwcOJDVq1ezfPlytm3bxtWrV2nZsiV37tz5v5gGcLcgf/nll6lbt266Y/Pnzyc8PJyIiAgOHjzIuHHj6NevH1999ZURM2rUKD744APee+89fvnlF/r27csLL7zAvn37/s/mICIiIiIiIo/mP1vQN2/enIkTJ9K2bdsMj5vNZmbOnMnIkSNp27YtFStWJCoqipSUFD799FMALl++zEcffcT06dNp3LgxgYGBfPLJJxw4cIDvvvsuw3FDQkKIiYlh1qxZmEwmTCYTx48fByAmJoann34aBwcHChcuzBtvvMHt27cfOJdRo0ZRvnx52rdvn+7YkiVL6NOnDx06dKBkyZJ07NiRnj17MmXKFIuYN998kxYtWlCyZElee+01goODmT59epbnjY2NpV69ejg5OeHj40NYWBjXrl0zjvv6+jJx4kS6du2Kq6srxYsXZ+3atZw/f57WrVvj6upKQEAAe/bsMfosWrQId3d31qxZQ9myZXF0dKRJkyacPHky0zzS0tIYP348RYsWxcHBgSpVqljsMHj22WcJDQ216HPx4kUcHBzYtGkTALdu3WL48OEUKVIEFxcXatasyZYtWx5pvvPmzaNMmTI4Ojri6enJiy++mOX1ExEREREReRz/2YL+QY4dO0ZSUhJNmzY12hwcHKhfvz6xsbEAxMXFkZqaahHj7e1NxYoVjZj7zZo1i6CgIHr37k1iYiKJiYn4+Phw+vRpWrRoQY0aNfjpp5+YP38+H330ERMnTswyz02bNrFy5Urmzp2b4fGbN2/i6Oho0ebk5MSPP/5IampqljHbtm3L9LwHDhwgODiYtm3bEh8fz4oVK9i2bVu6wvndd9+lTp067Nu3j+eee44uXbrQtWtXXnnlFfbu3Uvp0qXp2rUrZrPZ6JOSksJbb71FVFQU27dvJzk5mY4dO2aay6xZs5g+fTrvvPMO8fHxBAcH8/zzz3PkyBEAevXqxaeffsrNmzeNPkuXLsXb25uGDRsC0L17d7Zv387y5cuJj4/npZdeolmzZsYYD5rvnj17CAsLY/z48Rw6dIgNGzZQr169THO+efMmycnJFh8REREREZFHoYI+E0lJSQB4enpatHt6ehrHkpKSsLe3x8PDI9OY+7m5uWFvb4+zszNeXl54eXlhY2PDvHnz8PHxYc6cOZQvX542bdowbtw4pk+fTlpaWoZjXbx4kZCQEBYtWkS+fPkyjAkODubDDz8kLi4Os9nMnj17+Pjjj0lNTeXChQtGzIwZMzhy5AhpaWlER0ezdu1aEhMTM70+06ZNo1OnTgwcOJAyZcpQu3ZtZs+ezeLFi7lx44YR16JFC/r06UOZMmUYM2YMV65coUaNGrz00kuULVuWESNGkJCQwNmzZ40+qampzJkzh6CgIKpVq0ZUVBSxsbH8+OOPGebyzjvvMGLECDp27Ei5cuWYMmUKVapUYebMmQC0a9cOk8nE2rVrjT6RkZGEhIRgMpk4evQoy5YtY+XKldStW5dSpUoxdOhQnnnmGSIjIx9qvidOnMDFxYWWLVtSvHhxAgMDCQsLy/T6TZ48GTc3N+Pj4+OTaayIiIiIiEhGVNA/gMlksvhuNpvTtd3vYWLul5CQQFBQkEW/OnXqcPXq1Uxfste7d286deqU5Urw6NGjad68ObVq1cLOzo7WrVsTEhICgI2NDXB3hbtMmTKUL18ee3t7QkND6d69u3E8I3FxcSxatAhXV1fjExwcTFpaGseOHTPiKlWqZPz73s2RgICAdG3nzp0z2mxtbalevbrxvXz58ri7u5OQkJAuj+TkZM6cOUOdOnUs2uvUqWPEOzg48Morr/Dxxx8DsH//fn766SfjOuzduxez2UzZsmUt5hMTE8PRo0cfar5NmjShePHilCxZki5durB06VJSUlIyvX7h4eFcvnzZ+GT1SIGIiIiIiEhGbHM7gSeVl5cXcHcVvnDhwkb7uXPnjCLUy8uLW7ducenSJYtV+nPnzlG7du1HOl9GNwHubUPP7ObApk2b+PLLL3nnnXeM+LS0NGxtbVmwYAE9evTAycmJjz/+mA8++ICzZ89SuHBhFixYQN68eSlYsCAAhQoVYs2aNdy4cYOLFy/i7e3NG2+8QYkSJTLNNy0tjT59+mS4Cl2sWDHj33Z2dsa/780jo7b7dyFkNOesbpI86MZLr169qFKlCqdOneLjjz+mUaNGFC9e3Di3jY0NcXFx6W5iuLq6PtR87e3t2bt3L1u2bGHjxo2MGTOGiIgIdu/eneEvDzg4OODg4JDpfERERERERB5EBX0mSpQogZeXF9HR0QQGBgJ3X5wWExNjvFCuWrVq2NnZER0dbbyQLjExkZ9//pmpU6dmOra9vX26t+D7+/uzatUqi0I0NjaWvHnzUqRIkQzH2bFjh8U4a9euZcqUKcTGxqbrY2dnR9GiRQFYvnw5LVu2JE8eyw0ajo6OFClShNTUVFatWpXhS/buqVq1KgcPHqR06dKZxmTX7du32bNnD08//TQAhw4d4q+//qJ8+fLpYvPly4e3tzfbtm2z2KkQGxtr9Ie7uwKqV6/OwoUL+fTTT3nvvfeMY4GBgdy5c4dz585l+EsB8HDztbW1pXHjxjRu3JixY8fi7u7Opk2bMn3xooiIiIiIyOP4zxb0V69e5bfffjO+Hzt2jP3795M/f36KFSuGyWRi4MCBTJo0iTJlylCmTBkmTZqEs7MznTp1Au4+D9+zZ0+GDBlCgQIFyJ8/P0OHDiUgIIDGjRtnem5fX1927drF8ePHcXV1JX/+/Lz++uvMnDmT/v37ExoayqFDhxg7diyDBw9OV3jf4+fnZ/F9z5495MmTh4oVKxpthw8f5scff6RmzZpcunSJGTNm8PPPPxMVFWXE7Nq1i9OnT1OlShVOnz5NREQEaWlpDB8+PNM5jBgxglq1atGvXz969+6Ni4sLCQkJREdHWxTL2WFnZ0f//v2ZPXs2dnZ2hIaGUqtWLYsC/e+GDRvG2LFjKVWqFFWqVCEyMpL9+/ezdOlSi7hevXoRGhqKs7MzL7zwgtFetmxZOnfuTNeuXZk+fTqBgYFcuHCBTZs2ERAQQIsWLR4433Xr1vH7779Tr149PDw8WL9+PWlpaZQrV+6xroWIiIiIiEhm/rMF/Z49e4w3nAMMHjwYgG7durFo0SIAhg8fzvXr13n99de5dOkSNWvWZOPGjeTNm9fo9+6772Jra0v79u25fv06jRo1YtGiRVk+fz506FC6deuGv78/169f59ixY/j6+rJ+/XqGDRtG5cqVyZ8/Pz179mTUqFGPNc87d+4wffp0Dh06hJ2dHQ0bNiQ2NhZfX18j5saNG4waNYrff/8dV1dXWrRowZIlSzLcKn5PpUqViImJYeTIkdStWxez2UypUqXo0KHDY+UL4OzszIgRI+jUqROnTp3imWeeMZ5/z0hYWBjJyckMGTKEc+fO4e/vz5dffkmZMmUs4l5++WUGDhxIp06d0r3VPzIykokTJzJkyBBOnz5NgQIFCAoKokWLFg81X3d3d7744gsiIiK4ceMGZcqUYdmyZVSoUOGxr4eIiIiIiEhGTOa//16YSC5btGgRAwcO5K+//srxsU+ePImvry+7d++matWqOT7+40hOTsbNzY3Gz72BrZ3jgzuIiIiIyGP7ZvXY3E5BJJ17tcHly5cz/TWze/6zK/Ty35GamkpiYiJvvPEGtWrVeuKKeRERERERkezQz9bJv9727dspXrw4cXFxvP/++7mdjoiIiIiISI7QCr08UUJCQozfh88pDRo0QE+WiIiIiIjIv41W6EVERERERESskAp6ERERERERESukgl5ERERERETECqmgFxEREREREbFCKuhFRERERERErJAKehERERERERErpIJeRERERERExAqpoBcRERERERGxQiroRURERERERKyQbW4nICL/s+rTcPLly5fbaYiIiIiIiBXQCr2IiIiIiIiIFVJBLyIiIiIiImKFVNCLiIiIiIiIWCEV9CIiIiIiIiJWSAW9iIiIiIiIiBVSQS8iIiIiIiJihVTQi4iIiIiIiFghFfQiIiIiIiIiVkgFvYiIiIiIiIgVss3tBETkf4IHTMHW3jG30xARETH88MHo3E5BREQyoRV6ERERERERESukgl5ERERERETECqmgFxEREREREbFCKuhFRERERERErJAKehERERERERErpIJeRERERERExAqpoBcRERERERGxQiroRURERERERKyQCnoRERERERERK6SCXkRERERERMQKqaAXERERERERsUIq6EVERERERESskAp6ERERERERESukgl6sUoMGDRg4cGBupyEiIiIiIpJrcrWg37p1K61atcLb2xuTycSaNWvSxZjNZiIiIvD29sbJyYkGDRpw8ODBB469atUq/P39cXBwwN/fn9WrV6eLmTdvHiVKlMDR0ZFq1arxww8/5Mi5RURERERERP5puVrQX7t2jcqVKzNnzpxMY6ZOncqMGTOYM2cOu3fvxsvLiyZNmnDlypVM++zYsYMOHTrQpUsXfvrpJ7p06UL79u3ZtWuXEbNixQoGDhzIyJEj2bdvH3Xr1qV58+acOHHisc6dHampqTk6nrWdX0RERERERB5drhb0zZs3Z+LEibRt2zbD42azmZkzZzJy5Ejatm1LxYoViYqKIiUlhU8//TTTcWfOnEmTJk0IDw+nfPnyhIeH06hRI2bOnGnEzJgxg549e9KrVy/8/PyYOXMmPj4+zJ8//7HOnZaWxvjx4ylatCgODg5UqVKFDRs2GMePHz+OyWTis88+o0GDBjg6OvLJJ59kOJbJZOKDDz6gZcuWODs74+fnx44dO/jtt99o0KABLi4uBAUFcfToUYt+8+fPp1SpUtjb21OuXDmWLFmSbtz333+f1q1b4+LiwsSJEwH46quvqFatGo6OjpQsWZJx48Zx+/btTOcKEBkZiZ+fH46OjpQvX5558+ZlONe6devi5OREjRo1OHz4MLt376Z69eq4urrSrFkzzp8/b/QLCQmhTZs2jBs3jqeeeop8+fLRp08fbt26lWkely5domvXrnh4eODs7Ezz5s05cuQIcPfGUb58+fj8888t+nz11Ve4uLgYN2hOnz5Nhw4d8PDwoECBArRu3Zrjx48/9Hxv3bpFaGgohQsXxtHREV9fXyZPnpzl9RMREREREcmuJ/oZ+mPHjpGUlETTpk2NNgcHB+rXr09sbGym/Xbs2GHRByA4ONjoc+vWLeLi4tLFNG3a1IjJ7rlnzZrF9OnTeeedd4iPjyc4OJjnn3/eKC7vGTFiBGFhYSQkJBAcHJzpeBMmTKBr167s37+f8uXL06lTJ/r06UN4eDh79uwBIDQ01IhfvXo1AwYMYMiQIfz888/06dOH7t27s3nzZotxx44dS+vWrTlw4AA9evTg22+/5ZVXXiEsLIxffvmFDz74gEWLFvHWW29lmtvChQsZOXIkb731FgkJCUyaNInRo0cTFRWV7lyjRo1i79692Nra8vLLLzN8+HBmzZrFDz/8wNGjRxkzZoxFn++//56EhAQ2b97MsmXLWL16NePGjcs0l5CQEPbs2cOXX37Jjh07MJvNtGjRgtTUVFxcXOjYsSORkZEWfSIjI3nxxRfJmzcvKSkpNGzYEFdXV7Zu3cq2bduMmw33biQ8aL6zZ8/myy+/5LPPPuPQoUN88skn+Pr6ZpjvzZs3SU5OtviIiIiIiIg8CtvcTiArSUlJAHh6elq0e3p68scff2TZL6M+98a7cOECd+7cyTImu+d+5513GDFiBB07dgRgypQpbN68mZkzZzJ37lwjbuDAgZnuTPi77t270759e+DuTYCgoCBGjx5t3AQYMGAA3bt3tzh/SEgIr7/+OgCDBw9m586dvPPOOzRs2NCI69SpEz169DC+d+nShTfeeINu3boBULJkSSZMmMDw4cMZO3ZshrlNmDCB6dOnG/MoUaKEcTPg3jgAQ4cOtcj35Zdf5vvvv6dOnToA9OzZk0WLFlmMbW9vz8cff4yzszMVKlRg/PjxDBs2jAkTJpAnj+V9qCNHjvDll1+yfft2ateuDcDSpUvx8fFhzZo1vPTSS/Tq1YvatWtz5swZvL29uXDhAuvWrSM6OhqA5cuXkydPHj788ENMJhNwt+B3d3dny5YtNG3a9IHzPXHiBGXKlOGZZ57BZDJRvHjxTP+ukydPzvIGhYiIiIiIyIM80Sv099wrsO4xm83p2rLTJ6di7klOTubMmTNGoXpPnTp1SEhIsGirXr16lvnfU6lSJePf924uBAQEWLTduHHDWOFNSEjI1vnj4uIYP348rq6uxqd3794kJiaSkpKSLq/z589z8uRJevbsadFn4sSJ6R4BeJg5nDt3zqJP5cqVcXZ2Nr4HBQVx9epVTp48mS6XhIQEbG1tqVmzptFWoEABypUrZ8z76aefpkKFCixevBiAJUuWUKxYMerVq2fM/7fffiNv3rzGXPLnz8+NGzc4evToQ803JCSE/fv3U65cOcLCwti4cWO6XO8JDw/n8uXLxiejeYmIiIiIiGTliV6h9/LyAu6ulhcuXNhoP3fuXLqV8/v73Vthz6hPwYIFsbGxyTImu+eGh7sJ4OLikuUY99jZ2aUbN6O2tLS0xzp/Wloa48aNy3DXgKOjY7q2e+dbuHChRSENYGNj88hz+Hv+WcnoZorZbM4w9v559+rVizlz5vDGG28QGRlJ9+7dLa5ftWrVWLp0abpxChUqxI0bN4Cs51u1alWOHTvGN998w3fffUf79u1p3Lhxumf34e7jGw4ODg81ZxERERERkYw80Sv0JUqUwMvLy9gWDXeff4+JiTG2VmckKCjIog/Axo0bjT729vZUq1YtXUx0dLQRk51z58uXD29vb7Zt22bRHhsbi5+f30PM+PH5+fll6/xVq1bl0KFDlC5dOt3n/i3ucHdVvUiRIvz+++/p4kuUKPHY8/jpp5+4fv268X3nzp24urpStGjRdLH+/v7cvn3b4lcMLl68yOHDhy3m/corr3DixAlmz57NwYMHLR4LqFq1KkeOHOGpp55KNx83N7eHnm++fPno0KEDCxcuZMWKFaxatYo///zzsa+HiIiIiIjI/XJ1hf7q1av89ttvxvdjx46xf/9+8ufPT7FixTCZTAwcOJBJkyZRpkwZypQpw6RJk3B2dqZTp06ZjjtgwADq1avHlClTaN26NWvXruW7776zKHQHDx5Mly5dqF69OkFBQSxYsIATJ07Qt29fgGyfe9iwYYwdO5ZSpUpRpUoVIiMj2b9/f4Yrv/+EYcOG0b59e6pWrUqjRo346quv+OKLL/juu++y7DdmzBhatmyJj48PL730Enny5CE+Pp4DBw4Yb8G/X0REBGFhYeTLl4/mzZtz8+ZN9uzZw6VLlxg8ePBjzePWrVv07NmTUaNG8ccffzB27FhCQ0MzvLlQpkwZWrduTe/evfnggw/Imzcvb7zxBkWKFKF169ZGnIeHB23btmXYsGE0bdrU4uZA586dmTZtGq1btzZ+peDEiRN88cUXDBs2jKJFiz5wvu+++y6FCxemSpUq5MmTh5UrV+Ll5YW7u/tjXQsREREREZGM5GpBv2fPHosXtd0rArt162a8JG348OFcv36d119/nUuXLlGzZk02btxI3rx5jX4hISEcP36cLVu2AFC7dm2WL1/OqFGjGD16NKVKlWLFihUWW6U7dOjAxYsXGT9+PImJiVSsWJH169dbvMjsYc59v7CwMJKTkxkyZAjnzp3D39+fL7/8kjJlyuTEJXugNm3aMGvWLKZNm0ZYWBglSpQgMjKSBg0aZNkvODiYdevWMX78eKZOnYqdnR3ly5enV69emfbp1asXzs7OTJs2jeHDh+Pi4kJAQAADBw587Hk0atSIMmXKUK9ePW7evEnHjh2JiIjIND4yMpIBAwbQsmVLbt26Rb169Vi/fr3F1n64+wK+Tz/91OKFgADOzs5s3bqVESNG0LZtW65cuUKRIkVo1KgR+fLle6j5urq6MmXKFI4cOYKNjQ01atRg/fr1Gd6EEBEREREReVwmc2YPIFuRBg0a0KBBgywLPrEeISEh/PXXX6xZsybHx166dCkDBgzgzJkz2Nvb5/j42ZWcnIybmxu1Qt7E1j79OwtERERyyw8fjM7tFERE/lPu1QaXL182Fhcz80S/FO9hXLlyhaNHj7Ju3brcTkWeYCkpKRw7dozJkyfTp0+fJ6qYFxERERERyQ6r3wucN29eTp48iaura26nIk+wqVOnUqVKFTw9PQkPD8/tdERERERERB7bv2LLvYi105Z7ERF5UmnLvYjI/61H2XJv9Sv0IiIiIiIiIv9FKuhFRERERERErJAKehERERERERErpIJeRERERERExAqpoBcRERERERGxQiroRURERERERKyQCnoRERERERERK6SCXkRERERERMQKqaAXERERERERsUK2uZ2AiPzPt7NGkC9fvtxOQ0RERERErIBW6EVERERERESskAp6ERERERERESukgl5ERERERETECqmgFxEREREREbFCKuhFRERERERErJAKehERERERERErpIJeRERERERExAo9dkF/48aNnMhDRERERERERB5Btgr6tLQ0JkyYQJEiRXB1deX3338HYPTo0Xz00Uc5mqCIiIiIiIiIpGebnU4TJ04kKiqKqVOn0rt3b6M9ICCAd999l549e+ZYgiL/JfUj3sbGwTG30xAR+VfbM3lMbqcgIiKSI7K1Qr948WIWLFhA586dsbGxMdorVarEr7/+mmPJiYiIiIiIiEjGslXQnz59mtKlS6drT0tLIzU19bGTEhEREREREZGsZaugr1ChAj/88EO69pUrVxIYGPjYSYmIiIiIiIhI1rL1DP3YsWPp0qULp0+fJi0tjS+++IJDhw6xePFi1q1bl9M5ioiIiIiIiMh9srVC36pVK1asWMH69esxmUyMGTOGhIQEvvrqK5o0aZLTOYqIiIiIiIjIfbK1Qg8QHBxMcHBwTuYiIiIiIiIiIg8pWyv0PXr0ICoqKl17cnIyPXr0eOykRERERERERCRr2SroFy1axOuvv05YWBhpaWlG+/Xr1zMs9EVEREREREQkZ2WroAf4+uuv+eabbwgODubSpUs5mZOIiIiIiIiIPEC2C3p/f3927txJamoqNWrUICEhISfzEhEREREREZEsZKugN5lMABQoUIDvvvuOBg0aUKtWLb788sscTU5EREREREREMpatt9ybzeb/DWBry4cffoi/vz+vv/56jiUmIiIiIiIiIpnLVkG/efNm8ufPb9E2ePBgKlWqxPbt23MkMRERERERERHJXLa23NevXx9b2/T3Aho3bszYsWMfOymRrGzZsgWTycRff/2V26mIiIiIiIjkmocu6AcPHsy1a9eMf2f1+S/ZunUrrVq1wtvbG5PJxJo1a9LFmM1mIiIi8Pb2xsnJiQYNGnDw4MEsxw0JCaFNmzY5kuOAAQOoVq0aDg4OVKlSJcOYzz77jCpVquDs7Ezx4sWZNm1ajpxbRERERERE/hkPveV+3759pKamGv/OzL0X5v1XXLt2jcqVK9O9e3fatWuXYczUqVOZMWMGixYtomzZskycOJEmTZpw6NAh8ubN+4/naDab6dGjB7t27SI+Pj7d8W+++YbOnTvz3nvv0bRpUxISEujVqxdOTk6Ehob+4/mJiIiIiIjIo3voFfrNmzfj7u5u/Duzz6ZNm/6pXJ9IzZs3Z+LEibRt2zbD42azmZkzZzJy5Ejatm1LxYoViYqKIiUlhU8//TTDPhEREURFRbF27VpMJhMmk4ktW7YAcODAAZ599lmcnJwoUKAAr776KlevXs0yx9mzZ9OvXz9KliyZ4fElS5bQpk0b+vbtS8mSJXnuuecYMWIEU6ZMsXgB4v1Onz5Nhw4d8PDwoECBArRu3Zrjx48bx+/tMpg0aRKenp64u7szbtw4bt++zbBhw8ifPz9Fixbl448/NvocP34ck8nE8uXLqV27No6OjlSoUMGYf2ZWrVpFhQoVcHBwwNfXl+nTpxvHxo8fT0BAQLo+1apVY8yYMcb3yMhI/Pz8cHR0pHz58sybN++R5rtlyxaefvppXFxccHd3p06dOvzxxx9Z5i0iIiIiIpJd2f4d+r/7448/+OWXX0hLS8uJ4f5Vjh07RlJSEk2bNjXaHBwcqF+/PrGxsRn2GTp0KO3bt6dZs2YkJiaSmJhI7dq1SUlJoVmzZnh4eLB7925WrlzJd99999ir6Ddv3sTR0dGizcnJiVOnTmVakKakpNCwYUNcXV3ZunUr27Ztw9XVlWbNmnHr1i0jbtOmTZw5c4atW7cyY8YMIiIiaNmyJR4eHuzatYu+ffvSt29fTp48aTH+sGHDGDJkCPv27aN27do8//zzXLx4McNc4uLiaN++PR07duTAgQNEREQwevRoFi1aBECPHj345Zdf2L17t9EnPj6effv2ERISAsDChQsZOXIkb731FgkJCUyaNInRo0cTFRX1UPO9ffs2bdq0oX79+sTHx7Njxw5effXVTHes3Lx5k+TkZIuPiIiIiIjIo3ikgj4qKoqZM2datL366quULFmSgIAAKlasmK4w+69LSkoCwNPT06Ld09PTOHY/V1dXnJyccHBwwMvLCy8vL+zt7Vm6dCnXr19n8eLFVKxYkWeffZY5c+awZMkSzp49m+0cg4OD+eKLL/j+++9JS0vj8OHDxt85MTExwz7Lly8nT548fPjhhwQEBODn50dkZCQnTpywWE3Pnz8/s2fPply5cvTo0YNy5cqRkpLCm2++SZkyZQgPD8fe3j7dryOEhobSrl07/Pz8mD9/Pm5ubnz00UcZ5jJjxgwaNWrE6NGjKVu2LCEhIYSGhhrvAShatCjBwcFERkYafSIjI6lfv76xa2HChAlMnz6dtm3bUqJECdq2bcugQYP44IMPHmq+ycnJXL58mZYtW1KqVCn8/Pzo1q0bxYoVyzDnyZMn4+bmZnx8fHwe/IcSERERERH5m0cq6N9//33c3NyM7xs2bCAyMpLFixeze/duY0u1pHf/Sq3ZbH7k9w0kJCRQuXJlXFxcjLY6deqQlpbGoUOHsp1b7969CQ0NpWXLltjb21OrVi06duwIgI2NTYZ94uLi+O2338ibNy+urq64urqSP39+bty4wdGjR424ChUqkCfP//4z8/T0tNj+bmNjQ4ECBTh37pzF+EFBQca/bW1tqV69OgkJCRnmkpCQQJ06dSza6tSpw5EjR7hz544xx2XLlnHjxg1SU1NZunQpPXr0AOD8+fOcPHmSnj17GnNxdXVl4sSJxlweNN/8+fMTEhJCcHAwrVq1YtasWZneDAEIDw/n8uXLxkc3wkRERERE5FE90u/QHz58mOrVqxvf165dy/PPP0/nzp0BmDRpEt27d8/ZDK2cl5cXcHelvnDhwkb7uXPn0q3aP0hWNwEe52WEJpOJKVOmMGnSJJKSkihUqBDff/89AL6+vhn2SUtLo1q1aixdujTdsUKFChn/trOzS3eujNoe5nGNzOaY0XW5/9n/Vq1a4eDgwOrVq3FwcODmzZvGSwzvnXvhwoXUrFnTot+9GxoPM9/IyEjCwsLYsGEDK1asYNSoUURHR1OrVq10fRwcHHBwcHjgnEVERERERDLzSCv0169fJ1++fMb32NhY6tWrZ3wvWbJkptvI/6tKlCiBl5cX0dHRRtutW7eIiYmhdu3amfazt7c3Vpfv8ff3Z//+/cbPBwJs376dPHnyULZs2cfO1cbGhiJFimBvb8+yZcsICgriqaeeyjC2atWqHDlyhKeeeorSpUtbfP6+iyO7du7cafz79u3bxMXFUb58+Qxj/f392bZtm0VbbGwsZcuWNQpyW1tbunXrRmRkJJGRkXTs2BFnZ2fg7q6BIkWK8Pvvv6ebS4kSJR5pvoGBgYSHhxMbG0vFihUzffGhiIiIiIjI43qkgr548eLExcUBcOHCBQ4ePMgzzzxjHE9KSsqRYs6aXL16lf3797N//37g7kvw9u/fz4kTJ4C7q8oDBw5k0qRJrF69mp9//pmQkBCcnZ3p1KlTpuP6+voSHx/PoUOHuHDhAqmpqXTu3BlHR0e6devGzz//zObNm+nfvz9dunTJcrX/t99+Y//+/SQlJXH9+nUj33svr7tw4QLvv/8+v/76K/v372fAgAGsXLky3fsS/q5z584ULFiQ1q1b88MPP3Ds2DFiYmIYMGAAp06devQLeZ+5c+eyevVqfv31V/r168elS5eMLfL3GzJkCN9//z0TJkzg8OHDREVFMWfOHIYOHWoR16tXLzZt2sQ333yTbqyIiAgmT57MrFmzOHz4MAcOHCAyMpIZM2Y81HyPHTtGeHg4O3bs4I8//mDjxo0cPnwYPz+/x74WIiIiIiIiGXmkLfddu3alX79+HDx4kE2bNlG+fHmqVatmHL+3KvlfsmfPHho2bGh8Hzx4MADdunUz3rI+fPhwrl+/zuuvv86lS5eoWbMmGzduzPI36Hv37s2WLVuoXr06V69eZfPmzTRo0IBvv/2WAQMGUKNGDZydnWnXrp1RdGamV69exMTEGN8DAwOBuzcf7m2pj4qKYujQoZjNZoKCgoyfYMuMs7MzW7duZcSIEbRt25YrV65QpEgRGjVqZLGLI7vefvttpkyZwr59+yhVqhRr166lYMGCGcZWrVqVzz77jDFjxjBhwgQKFy7M+PHjjTfY31OmTBlq167NxYsX022t79WrF87OzkybNo3hw4fj4uJCQEAAAwcOfKj5Xr9+nV9//ZWoqCguXrxI4cKFCQ0NpU+fPo99LURERERERDJiMmf1Q+P3SUtLY+zYsaxbtw4vLy9mzJhhsQL50ksv0axZM3r27PmPJCv/fsePH6dEiRLs27ePKlWq5OjYZrOZ8uXL06dPH+PGy5MiOTkZNzc3qgwKx8bB8cEdREQk2/ZMHpPbKYiIiGTqXm1w+fLlBy6WPtIKfZ48eZgwYQITJkzI8PjKlSsfZTiR/zPnzp1jyZIlnD59Wi9uFBERERGRf4VHKujvCQkJoUePHhYvxBN5knl6elKwYEEWLFiAh4dHbqcjIiIiIiLy2LJV0F+5coWmTZvi4+ND9+7d6datG0WKFMnp3OQ/yNfXN91PzuWEf2JMERERERGR3PRIb7m/Z9WqVZw+fZrQ0FBWrlyJr68vzZs35/PPPyc1NTWncxQRERERERGR+2SroAcoUKAAAwYMYN++ffz444+ULl2aLl264O3tzaBBgzhy5EhO5ikiIiIiIiIif5Ptgv6exMRENm7cyMaNG7GxsaFFixYcPHgQf39/3n333ZzIUURERERERETuk62CPjU1lVWrVtGyZUuKFy/OypUrGTRoEImJiURFRbFx40aWLFnC+PHjczpfERERERERESGbL8UrXLgwaWlpvPzyy/z4448Z/l54cHAw7u7uj5meiIiIiIiIiGQkWwX9u+++y0svvYSjo2OmMR4eHhw7dizbiYmIiIiIiIhI5rJV0Hfp0iWn8xARERERERGRR5Ctgv7atWu8/fbbfP/995w7d460tDSL47///nuOJCciIiIiIiIiGctWQd+rVy9iYmLo0qULhQsXxmQy5XReIiIiIiIiIpIFk9lsNj9qJ3d3d77++mvq1KnzT+Qk8p+TnJyMm5sbly9fJl++fLmdjoiIiIiI5JJHqQ2y9bN1Hh4e5M+fP1vJiYiIiIiIiMjjy1ZBP2HCBMaMGUNKSkpO5yMiIiIiIiIiDyFbz9BPnz6do0eP4unpia+vL3Z2dhbH9+7dmyPJiYiIiIiIiEjGslXQt2nTJofTEBEREREREZFHka2X4olIztJL8UREREREBP4PXoonIiIiIiIiIrkrW1vu79y5w7vvvstnn33GiRMnuHXrlsXxP//8M0eSExEREREREZGMZWuFfty4ccyYMYP27dtz+fJlBg8eTNu2bcmTJw8RERE5nKKIiIiIiIiI3C9bBf3SpUtZuHAhQ4cOxdbWlpdffpkPP/yQMWPGsHPnzpzOUURERERERETuk60t90lJSQQEBADg6urK5cuXAWjZsiWjR4/OuexE/mNqz52EjaNDbqchIvJE+2nQuNxOQURE5ImQrRX6okWLkpiYCEDp0qXZuHEjALt378bBQcWIiIiIiIiIyD8tWwX9Cy+8wPfffw/AgAEDGD16NGXKlKFr16706NEjRxMUERERERERkfSyteX+7bffNv794osvUrRoUWJjYyldujTPP/98jiUnIiIiIiIiIhnLVkF/v1q1alGrVq2cGEpEREREREREHkK2C/pDhw7x3nvvkZCQgMlkonz58vTv359y5crlZH4iIiIiIiIikoFsPUP/+eefU7FiReLi4qhcuTKVKlVi7969VKxYkZUrV+Z0jiIiIiIiIiJyn2yt0A8fPpzw8HDGjx9v0T527FhGjBjBSy+9lCPJiYiIiIiIiEjGsrVCn5SURNeuXdO1v/LKKyQlJT12UiIiIiIiIiKStWwV9A0aNOCHH35I175t2zbq1q372EmJiIiIiIiISNayteX++eefZ8SIEcTFxRlvt9+5cycrV65k3LhxfPnllxaxIiIiIiIiIpKzTGaz2fyonfLkebiFfZPJxJ07dx45KZH/muTkZNzc3KgwaQQ2jg65nY6IyBPtp0HjcjsFERGRf8y92uDy5cvky5cvy9hsrdCnpaVlKzERERERERERyRnZeoZeRERERERERHJXtlboAX788Ue2bNnCuXPn0q3Yz5gx47ETE8mKyWRi9erVtGnTJrdTERERERERyRXZWqGfNGkStWrVIjIykj179rBv3z7js3///sdKaOvWrbRq1Qpvb29MJhNr1qx5qH4xMTFUq1YNR0dHSpYsyfvvv58uZtWqVfj7++Pg4IC/vz+rV69OFzNv3jxKlCiBo6Mj1apVy/Bt/n8XERFBlSpVHirHh3Xz5k2qVKmCyWR64PU8e/YsISEheHt74+zsTLNmzThy5IhFzNGjR3nhhRcoVKgQ+fLlo3379pw9e9YiZu/evTRp0gR3d3cKFCjAq6++ytWrV3N0XiIiIiIiIpJzslXQz5o1i48//piEhAS2bNnC5s2bjc+mTZseK6Fr165RuXJl5syZ89B9jh07RosWLahbty779u3jzTffJCwsjFWrVhkxO3bsoEOHDnTp0oWffvqJLl260L59e3bt2mXErFixgoEDBzJy5Ej27dtH3bp1ad68OSdOnHisOT2q4cOH4+3t/cA4s9lMmzZt+P3331m7di379u2jePHiNG7cmGvXrgF3r2fTpk0xmUxs2rSJ7du3c+vWLVq1amXsrDhz5gyNGzemdOnS7Nq1iw0bNnDw4EFCQkL+yWmKiIiIiIjIY8hWQZ8nTx7q1KmT07kA0Lx5cyZOnEjbtm0fus/7779PsWLFmDlzJn5+fvTq1YsePXrwzjvvGDEzZ86kSZMmhIeHU758ecLDw2nUqBEzZ840YmbMmEHPnj3p1asXfn5+zJw5Ex8fH+bPn5/heRctWsS4ceP46aefMJlMmEwmFi1aBMCJEydo3bo1rq6uma6KZ+Sbb75h48aNFrln5siRI+zcuZP58+dTo0YNypUrx7x587h69SrLli0DYPv27Rw/fpxFixYREBBAQEAAkZGR7N6927j5sm7dOuzs7Jg7dy7lypWjRo0azJ07l1WrVvHbb79lev5bt24xfPhwihQpgouLCzVr1mTLli0W18fd3Z1169ZRrlw5nJ2defHFF7l27RpRUVH4+vri4eFB//79LX4NwdfXlwkTJtCpUydcXV3x9vbmvffey/JaHDhwgGeffRYnJ6d0Owy2bt2KnZ0dSUlJFn2GDBlCvXr1jO+xsbHUq1cPJycnfHx8CAsLM26MPMx8//jjD1q1aoWHhwcuLi5UqFCB9evXZ5m3iIiIiIhIdmWroB80aBBz587N6VyybceOHTRt2tSiLTg4mD179pCampplTGxsLHC3WIuLi0sX07RpUyPmfh06dGDIkCFUqFCBxMREEhMT6dChg7Fy/ueffxITE0N0dDRHjx6lQ4cOWc7j7Nmz9O7dmyVLluDs7PzAed+8eRMAR0dHo83GxgZ7e3u2bdtmxJhMJhwc/vdTaI6OjuTJk8cixt7e3uLnCJ2cnACMmIx0796d7du3s3z5cuLj43nppZfSbflPSUlh9uzZLF++nA0bNrBlyxbatm3L+vXrWb9+PUuWLGHBggV8/vnnFmNPmzaNSpUqsXfvXsLDwxk0aBDR0dEZ5pGSkkKzZs3w8PBg9+7drFy5ku+++47Q0FAA6tWrR8mSJVmyZInR5/bt23zyySd0794duHtDIDg4mLZt2xIfH8+KFSvYtm2bMcbDzLdfv37cvHmTrVu3cuDAAaZMmYKrq2uGOd+8eZPk5GSLj4iIiIiIyKPI1kvxhg4dynPPPUepUqXw9/fHzs7O4vgXX3yRI8k9rKSkJDw9PS3aPD09uX37NhcuXKBw4cKZxtxbtb1w4QJ37tzJMuZ+Tk5OuLq6Ymtri5eXl9EeHR1NfHw8x44dw8fHB4AlS5ZQoUIFdu/eTY0aNdKNZTabCQkJoW/fvlSvXp3jx48/cN7ly5enePHihIeH88EHH+Di4sKMGTNISkoiMTERgFq1auHi4sKIESOYNGkSZrOZESNGkJaWZsQ8++yzDB48mGnTpjFgwACuXbvGm2++CWDE3O/o0aMsW7aMU6dOGY8HDB06lA0bNhAZGcmkSZMASE1NZf78+ZQqVQqAF198kSVLlnD27FlcXV3x9/enYcOGbN682eKGR506dXjjjTcAKFu2LNu3b+fdd9+lSZMm6XJZunQp169fZ/Hixbi4uAAwZ84cWrVqxZQpU/D09KRnz55ERkYybNgwAL7++mtSUlJo3749cPcGQqdOnRg4cCAAZcqUYfbs2dSvX5/58+dz+vTpB873xIkTtGvXjoCAAABKliyZ6d9u8uTJjBun31EWEREREZHsy9YKff/+/dm8eTNly5alQIECuLm5WXxyg8lksvhuNpvTtWcUc3/bw8Q8SEJCAj4+PkYxD+Dv74+7uzsJCQkZ9nnvvfdITk4mPDz8oc9jZ2fHqlWrOHz4MPnz58fZ2ZktW7bQvHlzbGxsAChUqBArV67kq6++wtXVFTc3Ny5fvkzVqlWNmAoVKhAVFcX06dNxdnbGy8uLkiVL4unpacTcb+/evZjNZsqWLYurq6vxiYmJ4ejRo0acs7OzUczD3Rskvr6+FivXnp6enDt3zmL8oKCgdN8zu3YJCQlUrlzZKObh7g2BtLQ0Dh06BEBISAi//fYbO3fuBODjjz+mffv2Rp+4uDgWLVpkMZfg4GDS0tI4duzYQ803LCyMiRMnUqdOHcaOHUt8fHxmfzrCw8O5fPmy8Tl58mSmsSIiIiIiIhnJ1gr94sWLWbVqFc8991xO55MtXl5e6VbRz507h62tLQUKFMgy5t6KfMGCBbGxscky5mFldhMgq5sDmzZtYufOnRZb4wGqV69O586diYqKyrBftWrV2L9/P5cvX+bWrVsUKlSImjVrUr16dSOmadOmHD16lAsXLmBra4u7uzteXl6UKFHCiOnUqROdOnXi7NmzuLi4YDKZmDFjhkXM36WlpWFjY0NcXFy6ov/vxfr9uzdMJlOGbff/9GFGMrt2WV3Xe+1PPfUUrVq1IjIykpIlS7J+/XqL59/T0tLo06cPYWFh6cYoVqwY8fHxD5xvr169CA4O5uuvv2bjxo1MnjyZ6dOn079//3RjOjg4pPtbi4iIiIiIPIpsFfT58+e3WHXNbUFBQXz11VcWbRs3bqR69epG8RgUFER0dDSDBg2yiKlduzYA9vb2VKtWjejoaF544QUjJjo6mtatW2d6bnt7e4sXusHd1fgTJ05w8uRJY5X+l19+4fLly/j5+WU4zuzZs5k4caLx/cyZMwQHB7NixQpq1qz5wGtwb2fEkSNH2LNnDxMmTEgXU7BgQeDuzYNz587x/PPPp4u5d/Pi448/xtHRMcMt7gCBgYHcuXOHc+fOUbdu3Qfm96juraT//Xv58uUzjPX39ycqKopr164ZK+7bt28nT548lC1b1ojr1asXHTt2pGjRopQqVcrixY5Vq1bl4MGDlC5dOsNzPOx8fXx86Nu3L3379iU8PJyFCxdmWNCLiIiIiIg8rmwV9BEREYwdO5bIyMiHennbo7h69arFm9WPHTvG/v37yZ8/P8WKFcuwT9++fZkzZw6DBw+md+/e7Nixg48++sh40zvAgAEDqFevHlOmTKF169asXbuW7777zuKlb4MHD6ZLly5Ur16doKAgFixYwIkTJ+jbt2+m+fr6+ho5Fi1alLx589K4cWMqVapE586dmTlzJrdv3+b111+nfv36Fivnf3f/3O6t+pYqVYqiRYtmev6VK1dSqFAhihUrxoEDBxgwYABt2rSxeLlfZGQkfn5+FCpUiB07djBgwAAGDRpEuXLljJg5c+ZQu3ZtXF1diY6OZtiwYbz99tu4u7tneN6yZcvSuXNnunbtyvTp0wkMDOTChQts2rSJgIAAWrRokWnOD2P79u1MnTqVNm3aEB0dzcqVK/n6668zjO3cuTNjx46lW7duREREcP78efr370+XLl0sdlcEBwfj5ubGxIkTGT9+vMUYI0aMoFatWvTr14/evXvj4uJCQkIC0dHRvPfeew8134EDB9K8eXPKli3LpUuX2LRpU6Y3cERERERERB5Xtp6hnz17Nt988w2enp4EBARQtWpVi8/j2LNnD4GBgQQGBgJ3i+zAwEDGjBljxERERODr62t8L1GihLGFukqVKkyYMIHZs2fTrl07I6Z27dosX76cyMhIKlWqxKJFi9Ktfnfo0IGZM2cyfvx4qlSpwtatW1m/fj3FixfPNN927drRrFkzGjZsSKFChVi2bBkmk4k1a9bg4eFBvXr1aNy4MSVLlmTFihWPdW3g7g2EiIgI43tiYiJdunShfPnyhIWF0aVLF4sbGQCHDh2iTZs2+Pn5MX78eEaOHJnuZ/F+/PFHmjRpQkBAAAsWLOCDDz7IcPv530VGRtK1a1eGDBlCuXLleP7559m1a5fFuwOya8iQIcTFxREYGMiECROYPn06wcHBGcY6Ozvz7bff8ueff1KjRg1efPFFGjVqxJw5cyzi8uTJQ0hICHfu3KFr164WxypVqkRMTAxHjhyhbt26BAYGMnr0aAoXLvzQ871z5w79+vXDz8+PZs2aGT8jKCIiIiIi8k8wme+9Pe4RPOjt3GPHjs12Qg8jJCQEwPjN9/+K69evkz9/ftavX0/Dhg1zO51/jK+vLwMHDjTeOJ+TevfuzdmzZ/nyyy9zfOzHkZycjJubGxUmjcDGUc/Wi4hk5adB+pUQERH597pXG1y+fJl8+fJlGZutLff/dMH+IDExMWzdujVXc8gNMTExPPvss//qYv6fcvnyZXbv3s3SpUtZu3ZtbqcjIiIiIiLy2LJV0N8TFxdHQkICJpMJf39/Y5v8P+3YsWP/J+d50jRr1oxmzZrldhpWqXXr1vz444/06dMn0xf9iYiIiIiIWJNsFfTnzp2jY8eObNmyBXd3d8xmM5cvX6Zhw4YsX76cQoUK5XSe8h9y/PjxHB/z7z9RJyIiIiIi8m+QrZfi9e/fn+TkZA4ePMiff/7JpUuX+Pnnn0lOTn7gi9RERERERERE5PFla4V+w4YNfPfddxY/yeXv78/cuXMtfi5NRERERERERP4Z2VqhT0tLw87OLl27nZ0daWlpj52UiIiIiIiIiGQtWwX9s88+y4ABAzhz5ozRdvr0aQYNGkSjRo1yLDkRERERERERyVi2Cvo5c+Zw5coVfH19KVWqFKVLl6ZEiRJcuXKF9957L6dzFBEREREREZH7ZOsZeh8fH/bu3Ut0dDS//vorZrMZf39/GjdunNP5iYiIiIiIiEgGHmmFftOmTfj7+5OcnAxAkyZN6N+/P2FhYdSoUYMKFSrwww8//COJioiIiIiIiMj/PFJBP3PmTHr37k2+fPnSHXNzc6NPnz7MmDEjx5ITERERERERkYw9UkH/008/0axZs0yPN23alLi4uMdOSkRERERERESy9kgF/dmzZzP8ubp7bG1tOX/+/GMnJSIiIiIiIiJZe6SX4hUpUoQDBw5QunTpDI/Hx8dTuHDhHElM5L8ott+bGT7SIiIiIiIicr9HWqFv0aIFY8aM4caNG+mOXb9+nbFjx9KyZcscS05EREREREREMmYym83mhw0+e/YsVatWxcbGhtDQUMqVK4fJZCIhIYG5c+dy584d9u7di6en5z+Zs8i/TnJyMm5ubly+fFkr9CIiIiIi/2GPUhs80pZ7T09PYmNjee211wgPD+fevQCTyURwcDDz5s1TMS8iIiIiIiLyf+CRCnqA4sWLs379ei5dusRvv/2G2WymTJkyeHh4/BP5iYiIiIiIiEgGHrmgv8fDw4MaNWrkZC4iIiIiIiIi8pAe6aV4IiIiIiIiIvJkUEEvIiIiIiIiYoVU0IuIiIiIiIhYoWw/Qy8iOa/N5xHYOjvkdhoiIrluY8fJuZ2CiIjIE08r9CIiIiIiIiJWSAW9iIiIiIiIiBVSQS8iIiIiIiJihVTQi4iIiIiIiFghFfQiIiIiIiIiVkgFvYiIiIiIiIgVUkEvIiIiIiIiYoVU0IuIiIiIiIhYIRX0IiIiIiIiIlZIBb2IiIiIiIiIFVJBLyIiIiIiImKFVNCLiIiIiIiIWCEV9CIiIiIiIiJWSAW9iIiIiIiIiBVSQf9/JCIigipVquR2Gv8aJpOJNWvW5HYaIiIiIiIiueaJLuhDQkIwmUzpPr/99ltup5alJ63YXLBgAQ0aNCBfvnyYTCb++uuvdDGXLl2iS5cuuLm54ebmRpcuXTKMExERERERkSfDE13QAzRr1ozExESLT4kSJXI7LQBSU1NzO4WHkpKSQrNmzXjzzTczjenUqRP79+9nw4YNbNiwgf3799OlS5f/wyxFRERERETkUTzxBb2DgwNeXl4WHxsbGwC++uorqlWrhqOjIyVLlmTcuHHcvn0bgCFDhtCqVStjnJkzZ2Iymfj666+NtnLlyvHBBx8Y3yMjI/Hz88PR0ZHy5cszb94849jx48cxmUx89tlnNGjQAEdHRz755JN0+fr6+gLwwgsvYDKZjO/3LFmyBF9fX9zc3OjYsSNXrlwxjm3YsIFnnnkGd3d3ChQoQMuWLTl69Gi6HL744gsaNmyIs7MzlStXZseOHVlew4EDB/LGG29Qq1atDI8nJCSwYcMGPvzwQ4KCgggKCmLhwoWsW7eOQ4cOZTrurVu3GD58OEWKFMHFxYWaNWuyZcsW4/iiRYtwd3dn3bp1lCtXDmdnZ1588UWuXbtGVFQUvr6+eHh40L9/f+7cuWNxDSdMmECnTp1wdXXF29ub9957L8s5HjhwgGeffRYnJycKFCjAq6++ytWrVwHYunUrdnZ2JCUlWfQZMmQI9erVM77HxsZSr149nJyc8PHxISwsjGvXrj30fP/44w9atWqFh4cHLi4uVKhQgfXr12eY782bN0lOTrb4iIiIiIiIPIonvqDPzLfffssrr7xCWFgYv/zyCx988AGLFi3irbfeAqBBgwb88MMPpKWlARATE0PBggWJiYkBICkpicOHD1O/fn0AFi5cyMiRI3nrrbdISEhg0qRJjB49mqioKIvzjhgxgrCwMBISEggODk6X1+7du4G7NwcSExON7wBHjx5lzZo1rFu3jnXr1hETE8Pbb79tHL927RqDBw9m9+7dfP/99+TJk4cXXnjBmMM9I0eOZOjQoezfv5+yZcvy8ssvGzcysmPHjh24ublRs2ZNo61WrVq4ubkRGxubab/u3buzfft2li9fTnx8PC+99BLNmjXjyJEjRkxKSgqzZ89m+fLlbNiwgS1bttC2bVvWr1/P+vXrWbJkCQsWLODzzz+3GHvatGlUqlSJvXv3Eh4ezqBBg4iOjs4wj3s7EDw8PNi9ezcrV67ku+++IzQ0FIB69epRsmRJlixZYvS5ffs2n3zyCd27dwfu3hAIDg6mbdu2xMfHs2LFCrZt22aM8TDz7devHzdv3mTr1q0cOHCAKVOm4OrqmmHOkydPNh5vcHNzw8fHJ9PrLCIiIiIikhGT2Ww253YSmQkJCeGTTz7B0dHRaGvevDkrV66kXr16NG/enPDwcOPYJ598wvDhwzlz5gyXL18mf/78/Pjjj1StWpVChQoxdOhQvvjiC3788UeWLVvGoEGDjFXbYsWKMWXKFF5++WVjvIkTJ7J+/XpiY2M5fvw4JUqUYObMmQwYMCDLvE0mE6tXr6ZNmzZGW0REBNOmTSMpKYm8efMCMHz4cLZu3crOnTszHOf8+fM89dRTHDhwgIoVKxo5fPjhh/Ts2ROAX375hQoVKpCQkED58uWzzGvLli00bNiQS5cu4e7ubrRPmjSJRYsWcfjwYYv4smXL0r17d4trfM/Ro0cpU6YMp06dwtvb22hv3LgxTz/9tDFm9+7d+e233yhVqhQAffv2ZcmSJZw9e9Yodps1a4avry/vv/8+cHeF3s/Pj2+++cYYt2PHjiQnJxsr3n+/xgsXLmTEiBGcPHkSFxcXANavX0+rVq04c+YMnp6eTJ06lUWLFvHLL78AsHbtWl555RWSkpJwcXGha9euODk5WezY2LZtG/Xr1+fatWucPn36gfOtVKkS7dq1Y+zYsVn+HeDuCv3NmzeN78nJyfj4+NDwo0HYOjs8sL+IyL/dxo6TczsFERGRXJGcnIybmxuXL18mX758Wcba/h/llG0NGzZk/vz5xvd7BVtcXBy7d+82VuQB7ty5w40bN0hJScHNzY0qVaqwZcsW7OzsyJMnD3369GHs2LFcuXKFLVu2GKvz58+f5+TJk/Ts2ZPevXsb492+fRs3NzeLfKpXr57tufj6+hrFPEDhwoU5d+6c8f3o0aOMHj2anTt3cuHCBWNl/sSJE1SsWNGIq1SpksUYAOfOnXtgQZ8Vk8mUrs1sNmfYDrB3717MZjNly5a1aL958yYFChQwvjs7OxvFPICnpye+vr4WK9eenp4W1wEgKCgo3feZM2dmmEtCQgKVK1c2/tsAqFOnDmlpaRw6dAhPT09CQkIYNWoUO3fupFatWnz88ce0b9/e4r+n3377jaVLl1rMPy0tjWPHjvHzzz8/cL5hYWG89tprbNy4kcaNG9OuXTuLv9XfOTg44OCgwl1ERERERLLviS/oXVxcKF26dLr2tLQ0xo0bR9u2bdMdu7ei36BBA7Zs2YK9vT3169fHw8ODChUqsH37drZs2cLAgQONseDutvu/bzsHjOf1/55PdtnZ2Vl8N5lMFtvpW7VqhY+PDwsXLsTb25u0tDQqVqzIrVu3Mh3nXsF9/7b8R+Hl5cXZs2fTtZ8/fx5PT88M+6SlpWFjY0NcXFy6a/T3Yj2jOT/oOmQms5sLWd14uNf+1FNP0apVKyIjIylZsiTr16+3eP49LS2NPn36EBYWlm6MYsWKER8f/8D59urVi+DgYL7++ms2btzI5MmTmT59Ov3793/g3ERERERERB7VE1/QZ6Zq1aocOnQow2L/ngYNGvDRRx9ha2tL48aNAahfvz7Lly+3eH7e09OTIkWK8Pvvv9O5c+fHzs3Ozs7iJW8P4+LFiyQkJPDBBx9Qt25d4O6W7/8LQUFBXL58mR9//JGnn34agF27dnH58mVq166dYZ/AwEDu3LnDuXPnjHxz0v2PIezcuTPTHQj+/v5ERUVx7do144bL9u3byZMnj8WKeq9evejYsSNFixalVKlS1KlTxzhWtWpVDh48mOl/Tw87Xx8fH/r27Uvfvn0JDw9n4cKFKuhFREREROQfYbUF/ZgxY2jZsiU+Pj689NJL5MmTh/j4eA4cOMDEiROBuy9Du3LlCl999ZXR1qBBA9q1a0ehQoXw9/c3xouIiCAsLIx8+fLRvHlzbt68yZ49e7h06RKDBw9+pNx8fX35/vvvqVOnDg4ODnh4eDywj4eHBwUKFGDBggUULlyYEydO8MYbbzzSeTOTlJREUlISv/32G3D3BXB58+alWLFi5M+fHz8/P5o1a0bv3r2NZ8hfffVVWrZsSbly5TIcs2zZsnTu3JmuXbsyffp0AgMDuXDhAps2bSIgIIAWLVo8Vs7bt29n6tSptGnThujoaFauXGnxCwV/17lzZ8aOHUu3bt2IiIjg/Pnz9O/fny5duljsMAgODsbNzY2JEycyfvx4izFGjBhBrVq16NevH71798bFxYWEhASio6N57733Hmq+AwcOpHnz5pQtW5ZLly6xadMm/Pz8Hus6iIiIiIiIZMZq33IfHBzMunXriI6OpkaNGtSqVYsZM2ZQvHhxI8bNzY3AwEDy589vFO9169YlLS3NWJ2/p1evXnz44YcsWrSIgIAA6tevz6JFi7L1m/fTp08nOjoaHx8fAgMDH6pPnjx5WL58OXFxcVSsWJFBgwYxbdq0Rz53Rt5//30CAwON9wPUq1ePwMBAvvzySyNm6dKlBAQE0LRpU5o2bUqlSpUs3gqfkcjISLp27cqQIUMoV64czz//PLt27cqRN7YPGTKEuLg4AgMDmTBhAtOnT8/wVwXg7nP63377LX/++Sc1atTgxRdfpFGjRsyZM8ciLk+ePISEhHDnzh26du1qcaxSpUrExMRw5MgR6tatS2BgIKNHjzbeUfAw871z5w79+vUzbpCUK1fO4qcPRUREREREctIT/ZZ7+W/y9fVl4MCBxjsOclLv3r05e/asxc2MJ8G9N1nqLfciInfpLfciIvJf9a96y71ITrh8+TK7d+9m6dKlrF27NrfTEREREREReWwq6OU/oXXr1vz444/06dOHJk2a5HY6IiIiIiIij00FvTxxjh8/nuNj/v0n6kRERERERP4NrPaleCIiIiIiIiL/ZSroRURERERERKyQCnoRERERERERK6SCXkRERERERMQKqaAXERERERERsUIq6EVERERERESskAp6ERERERERESukgl5ERERERETECqmgFxEREREREbFCtrmdgIj8z5oXI8iXL19upyEiIiIiIlZAK/QiIiIiIiIiVkgFvYiIiIiIiIgVUkEvIiIiIiIiYoVU0IuIiIiIiIhYIRX0IiIiIiIiIlZIBb2IiIiIiIiIFVJBLyIiIiIiImKFVNCLiIiIiIiIWCHb3E5ARP5n7A8DcXCxz+00RJ5Ibzd4P7dTEBEREXmiaIVeRERERERExAqpoBcRERERERGxQiroRURERERERKyQCnoRERERERERK6SCXkRERERERMQKqaAXERERERERsUIq6EVERERERESskAp6ERERERERESukgl5ERERERETECqmgFxEREREREbFCKuhFRERERERErJAKehERERERERErpIJeRERERERExAqpoBcRERERERGxQiroxepERERQpUqV3E5DREREREQkV/1nC/qIiAhMJpPFx8vLyyLGbDYTERGBt7c3Tk5ONGjQgIMHD1rE3Lx5k/79+1OwYEFcXFx4/vnnOXXqVJbnbtCgAQMHDsyReXz22WdUqVIFZ2dnihcvzrRp09LFzJ07Fz8/P5ycnChXrhyLFy+2OJ6amsr48eMpVaoUjo6OVK5cmQ0bNuRIfiIiIiIiIvLP+M8W9AAVKlQgMTHR+Bw4cMDi+NSpU5kxYwZz5sxh9+7deHl50aRJE65cuWLEDBw4kNWrV7N8+XK2bdvG1atXadmyJXfu3PnH8//mm2/o3Lkzffv25eeff2bevHlGvvfMnz+f8PBwIiIiOHjwIOPGjaNfv3589dVXRsyoUaP44IMPeO+99/jll1/o27cvL7zwAvv27fvH5yAiIiIiIiLZ858u6G1tbfHy8jI+hQoVMo6ZzWZmzpzJyJEjadu2LRUrViQqKoqUlBQ+/fRTAC5fvsxHH33E9OnTady4MYGBgXzyySccOHCA7777LsNzhoSEEBMTw6xZs4ydAcePHwcgJiaGp59+GgcHBwoXLswbb7zB7du3M81/yZIltGnThr59+1KyZEmee+45RowYwZQpUzCbzUZMnz596NChAyVLlqRjx4707NmTKVOmWIzz5ptv0qJFC0qWLMlrr71GcHAw06dPz/L6xcbGUq9ePZycnPDx8SEsLIxr164Zx319fZk4cSJdu3bF1dWV4sWLs3btWs6fP0/r1q1xdXUlICCAPXv2GH0WLVqEu7s7a9asoWzZsjg6OtKkSRNOnjyZaR5paWmMHz+eokWL4uDgQJUqVSx2GDz77LOEhoZa9Ll48SIODg5s2rQJgFu3bjF8+HCKFCmCi4sLNWvWZMuWLY8033nz5lGmTBkcHR3x9PTkxRdfzPL6iYiIiIiIPI7/dEF/5MgRvL29KVGiBB07duT33383jh07doykpCSaNm1qtDk4OFC/fn1iY2MBiIuLIzU11SLG29ubihUrGjH3mzVrFkFBQfTu3dvYGeDj48Pp06dp0aIFNWrU4KeffmL+/Pl89NFHTJw4MdP8b968iaOjo0Wbk5MTp06d4o8//sgy5scffyQ1NTXLmG3btmV67gMHDhAcHEzbtm2Jj49nxYoVbNu2LV3h/O6771KnTh327dvHc889R5cuXejatSuvvPIKe/fupXTp0nTt2tW4AQGQkpLCW2+9RVRUFNu3byc5OZmOHTtmmsusWbOYPn0677zzDvHx8QQHB/P8889z5MgRAHr16sWnn37KzZs3jT5Lly7F29ubhg0bAtC9e3e2b9/O8uXLiY+P56WXXqJZs2bGGA+a7549ewgLC2P8+PEcOnSIDRs2UK9evUxzvnnzJsnJyRYfERERERGRR/GfLehr1qzJ4sWL+fbbb1m4cCFJSUnUrl2bixcvApCUlASAp6enRT9PT0/jWFJSEvb29nh4eGQacz83Nzfs7e1xdnY2dgbY2Ngwb948fHx8mDNnDuXLl6dNmzaMGzeO6dOnk5aWluFYwcHBfPHFF3z//fekpaVx+PBhZs6cCUBiYqIR8+GHHxIXF4fZbGbPnj18/PHHpKamcuHCBSNmxowZHDlyhLS0NKKjo1m7dq0xRkamTZtGp06dGDhwIGXKlKF27drMnj2bxYsXc+PGDSOuRYsW9OnThzJlyjBmzBiuXLlCjRo1eOmllyhbtiwjRowgISGBs2fPGn1SU1OZM2cOQUFBVKtWjaioKGJjY/nxxx8zzOWdd95hxIgRdOzYkXLlyjFlyhSqVKliXIt27dphMplYu3at0ScyMpKQkBBMJhNHjx5l2bJlrFy5krp161KqVCmGDh3KM888Q2Rk5EPN98SJE7i4uNCyZUuKFy9OYGAgYWFhmV6/yZMn4+bmZnx8fHwyjRUREREREcnIf7agb968Oe3atSMgIIDGjRvz9ddfAxAVFWURZzKZLL6bzeZ0bfd7mJj7JSQkEBQUZNGvTp06XL16NdOX7PXu3ZvQ0FBatmyJvb09tWrVMlaybWxsABg9ejTNmzenVq1a2NnZ0bp1a0JCQixiZs2aRZkyZShfvjz29vaEhobSvXt343hG4uLiWLRoEa6ursYnODiYtLQ0jh07ZsRVqlTJ+Pe9myMBAQHp2s6dO2e02draUr16deN7+fLlcXd3JyEhIV0eycnJnDlzhjp16li016lTx4h3cHDglVde4eOPPwZg//79/PTTT8Z12Lt3L2azmbJly1rMJyYmhqNHjz7UfJs0aULx4sUpWbIkXbp0YenSpaSkpGR6/cLDw7l8+bLxyeqRAhERERERkYzY5nYCTwoXFxcCAgKMLdb33niflJRE4cKFjbhz584ZRaiXlxe3bt3i0qVLFqv0586do3bt2o90/oxuAtzbhp7ZzQGTycSUKVOYNGkSSUlJFCpUiO+//x64+/w63N06//HHH/PBBx9w9uxZChcuzIIFC8ibNy8FCxYEoFChQqxZs4YbN25w8eJFvL29eeONNyhRokSm+aalpdGnT58MV6GLFStm/NvOzs4i38za7t+FkNGcs7pJ8qAbL7169aJKlSqcOnWKjz/+mEaNGlG8eHHj3DY2NsTFxaW7ieHq6vpQ87W3t2fv3r1s2bKFjRs3MmbMGCIiIti9ezfu7u7p+jg4OODg4JDpfERERERERB7kP7tCf7+bN2+SkJBgFO8lSpTAy8uL6OhoI+bWrVvExMQYxXq1atWws7OziElMTOTnn3/OsqC3t7dP9xZ8f39/YmNjLZ4lj42NJW/evBQpUiTL3G1sbChSpAj29vYsW7aMoKAgnnrqKYsYOzs7ihYtio2NDcuXL6dly5bkyWP553d0dKRIkSLcvn2bVatW0bp160zPWbVqVQ4ePEjp0qXTfezt7bPM90Fu375t8aK8Q4cO8ddff1G+fPl0sfny5cPb2zvd8/6xsbH4+fkZ3wMCAqhevToLFy7k008/pUePHsaxwMBA7ty5w7lz59LN5d6NnYeZr62tLY0bN2bq1KnEx8dz/Phx46V7IiIiIiIiOe0/u0I/dOhQWrVqRbFixTh37hwTJ04kOTmZbt26AXdXfAcOHMikSZMoU6YMZcqUYdKkSTg7O9OpUyfg7vPwPXv2ZMiQIRQoUID8+fMzdOhQYxt/Znx9fdm1axfHjx/H1dWV/Pnz8/rrrzNz5kz69+9PaGgohw4dYuzYsQwePDhd4X3PhQsX+Pzzz2nQoAE3btwgMjKSlStXEhMTY8QcPnyYH3/8kZo1a3Lp0iVmzJjBzz//bPFowa5duzh9+jRVqlTh9OnTREREkJaWxvDhwzOdw4gRI6hVqxb9+vWjd+/euLi4kJCQQHR0NO+9994j/S3uZ2dnR//+/Zk9ezZ2dnaEhoZSq1Ytnn766Qzjhw0bxtixYylVqhRVqlQhMjKS/fv3s3TpUou4Xr16ERoairOzMy+88ILRXrZsWTp37kzXrl2ZPn06gYGBXLhwgU2bNhEQEECLFi0eON9169bx+++/U69ePTw8PFi/fj1paWmUK1fusa6FiIiIiIhIZv6zBf2pU6d4+eWXuXDhAoUKFaJWrVrs3LnT2IYNMHz4cK5fv87rr7/OpUuXqFmzJhs3biRv3rxGzLvvvoutrS3t27fn+vXrNGrUiEWLFmX5/PnQoUPp1q0b/v7+XL9+nWPHjuHr68v69esZNmwYlStXJn/+/PTs2ZNRo0ZlOY+oqCiGDh2K2WwmKCiILVu2WBS+d+7cYfr06Rw6dAg7OzsaNmxIbGyssSUf4MaNG4waNYrff/8dV1dXWrRowZIlSzLcKn5PpUqViImJYeTIkdStWxez2UypUqXo0KFDlvk+DGdnZ0aMGEGnTp04deoUzzzzjPH8e0bCwsJITk5myJAhnDt3Dn9/f7788kvKlCljEffyyy8zcOBAOnXqlO6t/pGRkUycOJEhQ4Zw+vRpChQoQFBQEC1atHio+bq7u/PFF18QERHBjRs3KFOmDMuWLaNChQqPfT1EREREREQyYjL/fY+3SC5btGgRAwcO5K+//srxsU+ePImvry+7d++matWqOT7+40hOTsbNzY2B67rj4PJ4jyyI/Fu93eD93E5BRERE5B93rza4fPky+fLlyzL2P7tCL/8dqampJCYm8sYbb1CrVq0nrpgXERERERHJDr0UT/71tm/fTvHixYmLi+P997XCJyIiIiIi/w5aoZcnSkhIiPH78DmlQYMG6MkSERERERH5t9EKvYiIiIiIiIgVUkEvIiIiIiIiYoVU0IuIiIiIiIhYIRX0IiIiIiIiIlZIBb2IiIiIiIiIFVJBLyIiIiIiImKFVNCLiIiIiIiIWCEV9CIiIiIiIiJWSAW9iIiIiIiIiBWyze0EROR/xtWdSb58+XI7DRERERERsQJaoRcRERERERGxQiroRURERERERKyQCnoRERERERERK6SCXkRERERERMQKqaAXERERERERsUIq6EVERERERESskAp6ERERERERESukgl5ERERERETECtnmdgIi8j9f7G6Js4v+Z/lf177WptxOQURERESsgFboRURERERERKyQCnoRERERERERK6SCXkRERERERMQKqaAXERERERERsUIq6EVERERERESskAp6ERERERERESukgl5ERERERETECqmgFxEREREREbFCKuhFRERERERErJAKehERERERERErpIJeRERERERExAqpoBcRERERERGxQiroRURERERERKyQCnoRERERERERK6SCXqySyWRizZo1uZ2GiIiIiIhIrnniCvqIiAhMJpPFx8vL64H9YmJiqFatGo6OjpQsWZL3338/XcyqVavw9/fHwcEBf39/Vq9enS5m3rx5lChRAkdHR6pVq8YPP/zwwHyrVKny0PPLyt69e2nSpAnu7u4UKFCAV199latXr2bZ5+zZs4SEhODt7Y2zszPNmjXjyJEjFjFHjx7lhRdeoFChQuTLl4/27dtz9uzZxz63iIiIiIiI5J4nrqAHqFChAomJicbnwIEDWcYfO3aMFi1aULduXfbt28ebb75JWFgYq1atMmJ27NhBhw4d6NKlCz/99BNdunShffv27Nq1y4hZsWIFAwcOZOTIkezbt4+6devSvHlzTpw48Y/N9Z4zZ87QuHFjSpcuza5du9iwYQMHDx4kJCQk0z5ms5k2bdrw+++/s3btWvbt20fx4sVp3Lgx165dA+DatWs0bdoUk8nEpk2b2L59O7du3aJVq1akpaVl+9wiIiIiIiKSu57Igt7W1hYvLy/jU6hQoSzj33//fYoVK8bMmTPx8/OjV69e9OjRg3feeceImTlzJk2aNCE8PJzy5csTHh5Oo0aNmDlzphEzY8YMevbsSa9evfDz82PmzJn4+Pgwf/78DM+7aNEixo0bx08//WTsJli0aBEAJ06coHXr1ri6uma6Kv5369atw87Ojrlz51KuXDlq1KjB3LlzWbVqFb/99luGfY4cOcLOnTuZP38+NWrUoFy5csybN4+rV6+ybNkyALZv387x48dZtGgRAQEBBAQEEBkZye7du9m0aVO2zw1w69Ythg8fTpEiRXBxcaFmzZps2bLF4vq4u7uzbt06ypUrh7OzMy+++CLXrl0jKioKX19fPDw86N+/P3fu3DH6+fr6MmHCBDp16oSrqyve3t689957meYBcODAAZ599lmcnJzS7TDYunUrdnZ2JCUlWfQZMmQI9erVM77HxsZSr149nJyc8PHxISwszLgx8jDz/eOPP2jVqhUeHh64uLhQoUIF1q9fn2XeIiIiIiIi2fVEFvRHjhzB29ubEiVK0LFjR37//fcs43fs2EHTpk0t2oKDg9mzZw+pqalZxsTGxgJ3i7W4uLh0MU2bNjVi7tehQweGDBlisaOgQ4cOxsr5n3/+SUxMDNHR0Rw9epQOHTpkOoebN29ib29Pnjz/+5M4OTkBsG3btkz7ADg6OhptNjY22NvbG31u3ryJyWTCwcHBiHF0dCRPnjwWMY96boDu3buzfft2li9fTnx8PC+99FK6Lf8pKSnMnj2b5cuXs2HDBrZs2ULbtm1Zv34969evZ8mSJSxYsIDPP//cYuxp06ZRqVIl9u7dS3h4OIMGDSI6OjrDPFJSUmjWrBkeHh7s3r2blStX8t133xEaGgpAvXr1KFmyJEuWLDH63L59m08++YTu3bsDd28IBAcH07ZtW+Lj41mxYgXbtm0zxniY+fbr14+bN2+ydetWDhw4wJQpU3B1dc0w55s3b5KcnGzxEREREREReRRPXEFfs2ZNFi9ezLfffsvChQtJSkqidu3aXLx4MdM+SUlJeHp6WrR5enpy+/ZtLly4kGXMvVXbCxcucOfOnSxj7ufk5ISrq6vFjgInJye+++474uPj+fTTT6lWrRo1a9ZkyZIlxMTEsHv37gzHevbZZ0lKSmLatGncunWLS5cu8eabbwKQmJiYYZ/y5ctTvHhxwsPDuXTpErdu3eLtt98mKSnJ6FOrVi1cXFwYMWIEKSkpXLt2jWHDhpGWlmbEZOfcR48eZdmyZaxcuZK6detSqlQphg4dyjPPPENkZKQRl5qayvz58wkMDKRevXq8+OKLbNu2jY8++gh/f39atmxJw4YN2bx5s8X4derU4Y033qBs2bL079+fF198kXfffTfDXJYuXcr169dZvHgxFStW5Nlnn2XOnDksWbLE2BXRs2dPi7y+/vprUlJSaN++PXD3BkKnTp0YOHAgZcqUoXbt2syePZvFixdz48aNh5rviRMnqFOnDgEBAZT8f+3de1BTZ/4G8CcQ7mKQa4yKgDdQLiK41kuLiAqt11VbqxRwnO5KFQG1Fjuuq2Ntkc7YWpde1FVax7YwjNS1halAVbQK4oJZRVGRRVEXzVo16KqI5P390eH8GrmKhBD7fGYy05z3e855zwMlfnOSc7y8MHXqVL1PAPxWcnIyFAqF9OjXr1+zdURERERERC3pdg39yy+/jNmzZ8PPzw8TJ05EdnY2AOCrr75qdT2ZTKb3XAjRZHlzNU8ua09NW8rLy9GvXz+9Jm3o0KFwcHBAeXl5s+sMGzYMX331FTZt2gRbW1solUp4eXnBzc0N5ubmza5jYWGBPXv24MKFC3B0dIStrS0OHTqEl19+WVrHxcUFmZmZ+P7779GjRw8oFApotVqMGDFCqunIvktLSyGEwODBg9GjRw/pUVBQgMrKSqnO1tYWAwYMkJ67ubnBw8ND78y1m5sbNBqN3vZHjx7d5HlL2ZWXlyMgIAB2dnbSsrFjx0Kn0+H8+fMAgAULFuDixYsoKioCAOzcuROvvfaatE5JSQm+/PJLvWMJDw+HTqdDVVVVu443Pj4eGzZswNixY7F27VqcOnWq2fkCwLvvvgutVis9rly50mItERERERFRc+TGnkBb7Ozs4Ofn1+TK7b+lVCqbnEXXaDSQy+VwcnJqtabxjLyzszPMzc1brWmvlt4EaOvNgfnz52P+/Pm4ceMG7OzsIJPJ8NFHH8HT07PFdYKCgqBWq6HVavHo0SO4uLhg1KhRCA4OlmomT56MyspK3Lx5E3K5HA4ODlAqlXrbfdp963Q6mJubo6SkpEnT/9tm3cLCQm9MJpM1u6zxAn2taSm71nJtXO7q6opp06YhLS0NXl5eyMnJ0fv+u06nw6JFixAfH99kG+7u7jh16lSbx/vmm28iPDwc2dnZyM3NRXJyMjZt2oSlS5c22aaVlZXe1yCIiIiIiIieVrc7Q/+kuro6lJeXo3fv3i3WjB49usn3q3NzcxEcHCw1jy3VjBkzBgBgaWmJoKCgJjV5eXlSTXMsLS31LugG/Ho2vrq6Wu+s69mzZ6HVauHj49PK0f7Kzc0NPXr0QEZGBqytrTFp0qQ211EoFHBxcUFFRQX++c9/YsaMGU1qnJ2d4eDggAMHDkCj0WD69Okd3ndgYCAaGhqg0WgwcOBAvUd7bjPYlsYz6b997u3t3Wzt0KFDoVar9S5gd/ToUZiZmWHw4MHSsjfffBPp6enYunUrBgwYgLFjx0pjI0aMwJkzZ5ocy8CBA2Fpadnu4+3Xrx9iY2ORlZWFFStWYPv27c+cBRERERERUXO6XUP/9ttvo6CgAFVVVTh+/DjmzJmD2tpaxMTEtLhObGwsLl++jOXLl6O8vBw7d+7Ejh078Pbbb0s1CQkJyM3NRUpKCs6dO4eUlBTk5+cjMTFRqlm+fDn+/ve/Y+fOnSgvL8eyZctQXV2N2NjYFvft4eGBqqoqqNVq3Lx5E3V1dZg4cSL8/f0RGRmJ0tJSFBcXIzo6GiEhIXpnzp+UmpqK0tJSXLhwAZ9++ini4uKQnJwMBweHFtfJzMzEoUOHpFvXTZo0CTNnztS7uF9aWhqKiopQWVmJ3bt349VXX8WyZcswZMiQDu978ODBiIyMRHR0NLKyslBVVYUTJ04gJSWlU67sfvToUXz44YfSfDIzM5GQkNBsbWRkJKytrRETE4OysjIcPHgQS5cuRVRUlN6nK8LDw6FQKLBhwwbpYniNkpKSUFhYiCVLlkCtVqOiogL79u2Tzq6353gTExOxf/9+6SP6Bw4caNcbOERERERERB3R7Rr6q1evYt68eRgyZAhmzZoFS0tLFBUVoX///lLNunXr4OHhIT339PSUPkI9fPhwvPfee9iyZQtmz54t1YwZMwbp6elIS0uDv78/vvzyS2RkZGDUqFFSzdy5c7F582asX78ew4cPx+HDh5GTk6O37yfNnj0bERERCA0NhYuLC7799lvIZDLs3bsXvXr1wksvvYSJEyfCy8sLGRkZrR57cXExJk2aBD8/P2zbtg1bt25t8hFwDw8PrFu3TnpeU1ODqKgoeHt7Iz4+HlFRUdIt6xqdP38eM2fOhI+PD9avX4/Vq1fr3dKvvft+UlpaGqKjo7FixQoMGTIE06dPx/HjxzvlAm8rVqxASUkJAgMD8d5772HTpk0IDw9vttbW1hb79+/HrVu3MHLkSMyZMwdhYWFITU3VqzMzM8OCBQvQ0NCA6OhovTF/f38UFBSgoqICL774IgIDA7FmzRq9T4a0dbwNDQ1YsmQJfHx8EBERId1GkIiIiIiIyBBkovHqcSZkwYIFACDd8/334sGDB3B0dEROTg5CQ0ONPR2D8fDwQGJiot6nJzrLn/70J9y4cQP79u3r9G0/i9raWigUCqTlvwhbu25/aQsysNdeOGDsKRARERGRkTT2BlqtFj179my11iQ7h4KCAhw+fNjY0+hyBQUFmDBhwnPdzBuKVqvFiRMn8PXXX+Mf//iHsadDRERERET0zEyyoa+qqjL2FIwiIiICERERxp6GSZoxYwaKi4uxaNGidl1kkIiIiIiIqLszyYaenm+XLl3q9G3+9hZ1REREREREz4Nud1E8IiIiIiIiImobG3oiIiIiIiIiE8SGnoiIiIiIiMgEsaEnIiIiIiIiMkFs6ImIiIiIiIhMEBt6IiIiIiIiIhPEhp6IiIiIiIjIBLGhJyIiIiIiIjJBbOiJiIiIiIiITJDc2BMgov83a+QP6Nmzp7GnQUREREREJoBn6ImIiIiIiIhMEBt6IiIiIiIiIhPEhp6IiIiIiIjIBPE79ETdgBACAFBbW2vkmRARERERkTE19gSNPUJr2NATdQO//PILAKBfv35GngkREREREXUHd+/ehUKhaLWGDT1RN+Do6AgAqK6ubvN/Wno6tbW16NevH65cucI7CHQyZms4zNYwmKvhMFvDYbaGw2wNh9k+GyEE7t69C5VK1WYtG3qibsDM7NfLWSgUCv7RM5CePXsyWwNhtobDbA2DuRoOszUcZms4zNZwmG3HtfckHy+KR0RERERERGSC2NATERERERERmSA29ETdgJWVFdauXQsrKytjT+W5w2wNh9kaDrM1DOZqOMzWcJit4TBbw2G2XUcm2nMtfCIiIiIiIiLqVniGnoiIiIiIiMgEsaEnIiIiIiIiMkFs6ImIiIiIiIhMEBt6IiIiIiIiIhPEhp7IyD777DN4enrC2toaQUFBOHLkiLGn1K0kJydj5MiRsLe3h6urK2bOnInz58/r1QghsG7dOqhUKtjY2GD8+PE4c+aMXk1dXR2WLl0KZ2dn2NnZYfr06bh69apeze3btxEVFQWFQgGFQoGoqCjcuXPH0IfYbSQnJ0MmkyExMVFaxmw77tq1a3jjjTfg5OQEW1tbDB8+HCUlJdI4s+2Yx48f4y9/+Qs8PT1hY2MDLy8vrF+/HjqdTqphtu1z+PBhTJs2DSqVCjKZDHv37tUb78ocq6urMW3aNNjZ2cHZ2Rnx8fF49OiRIQ67S7SWbX19PZKSkuDn5wc7OzuoVCpER0fjP//5j942mG1Tbf3O/taiRYsgk8mwefNmveXMtXntyba8vBzTp0+HQqGAvb09XnjhBVRXV0vjzNZIBBEZTXp6urCwsBDbt28XZ8+eFQkJCcLOzk5cvnzZ2FPrNsLDw0VaWpooKysTarVaTJkyRbi7u4t79+5JNRs3bhT29vZiz5494vTp02Lu3Lmid+/eora2VqqJjY0Vffr0EXl5eaK0tFSEhoaKgIAA8fjxY6kmIiJC+Pr6imPHjoljx44JX19fMXXq1C49XmMpLi4WHh4ewt/fXyQkJEjLmW3H3Lp1S/Tv318sWLBAHD9+XFRVVYn8/Hxx8eJFqYbZdsyGDRuEk5OT+OGHH0RVVZXIzMwUPXr0EJs3b5ZqmG375OTkiNWrV4s9e/YIAOK7777TG++qHB8/fix8fX1FaGioKC0tFXl5eUKlUom4uDiDZ2AorWV7584dMXHiRJGRkSHOnTsnCgsLxahRo0RQUJDeNphtU239zjb67rvvREBAgFCpVOLjjz/WG2OuzWsr24sXLwpHR0excuVKUVpaKiorK8UPP/wgbty4IdUwW+NgQ09kRH/4wx9EbGys3jJvb2+xatUqI82o+9NoNAKAKCgoEEIIodPphFKpFBs3bpRqHj58KBQKhfjiiy+EEL/+48nCwkKkp6dLNdeuXRNmZmbixx9/FEIIcfbsWQFAFBUVSTWFhYUCgDh37lxXHJrR3L17VwwaNEjk5eWJkJAQqaFnth2XlJQkxo0b1+I4s+24KVOmiIULF+otmzVrlnjjjTeEEMy2o578B3xX5piTkyPMzMzEtWvXpJpvv/1WWFlZCa1Wa5Dj7UqtNZ6NiouLBQDpDX1m27aWcr169aro06ePKCsrE/3799dr6Jlr+zSX7dy5c6W/s81htsbDj9wTGcmjR49QUlKCyZMn6y2fPHkyjh07ZqRZdX9arRYA4OjoCACoqqrC9evX9XK0srJCSEiIlGNJSQnq6+v1alQqFXx9faWawsJCKBQKjBo1Sqp54YUXoFAonvufx5IlSzBlyhRMnDhRbzmz7bh9+/YhODgYr776KlxdXREYGIjt27dL48y248aNG4effvoJFy5cAAD861//ws8//4xXXnkFALPtLF2ZY2FhIXx9faFSqaSa8PBw1NXV6X1N5Xmm1Wohk8ng4OAAgNl2lE6nQ1RUFFauXIlhw4Y1GWeuHaPT6ZCdnY3BgwcjPDwcrq6uGDVqlN7H8pmt8bChJzKSmzdvoqGhAW5ubnrL3dzccP36dSPNqnsTQmD58uUYN24cfH19AUDKqrUcr1+/DktLS/Tq1avVGldX1yb7dHV1fa5/Hunp6SgtLUVycnKTMWbbcf/+97/x+eefY9CgQdi/fz9iY2MRHx+PXbt2AWC2zyIpKQnz5s2Dt7c3LCwsEBgYiMTERMybNw8As+0sXZnj9evXm+ynV69esLS0/F1k/fDhQ6xatQrz589Hz549ATDbjkpJSYFcLkd8fHyz48y1YzQaDe7du4eNGzciIiICubm5+OMf/4hZs2ahoKAAALM1JrmxJ0D0eyeTyfSeCyGaLKNfxcXF4dSpU/j555+bjHUkxydrmqt/nn8eV65cQUJCAnJzc2Ftbd1iHbN9ejqdDsHBwfjggw8AAIGBgThz5gw+//xzREdHS3XM9ullZGRg9+7d+OabbzBs2DCo1WokJiZCpVIhJiZGqmO2naOrcvy9Zl1fX4/XX38dOp0On332WZv1zLZlJSUl+OSTT1BaWvrUx8ZcW9d40dEZM2Zg2bJlAIDhw4fj2LFj+OKLLxASEtLiuszW8HiGnshInJ2dYW5u3uTdRo1G0+SdSQKWLl2Kffv24eDBg+jbt6+0XKlUAkCrOSqVSjx69Ai3b99utebGjRtN9vvf//73uf15lJSUQKPRICgoCHK5HHK5HAUFBdiyZQvkcrl03Mz26fXu3RtDhw7VW+bj4yNdDZi/tx23cuVKrFq1Cq+//jr8/PwQFRWFZcuWSZ8yYbadoytzVCqVTfZz+/Zt1NfXP9dZ19fX47XXXkNVVRXy8vKks/MAs+2II0eOQKPRwN3dXXpNu3z5MlasWAEPDw8AzLWjnJ2dIZfL23xdY7bGwYaeyEgsLS0RFBSEvLw8veV5eXkYM2aMkWbV/QghEBcXh6ysLBw4cACenp56456enlAqlXo5Pnr0CAUFBVKOQUFBsLCw0KupqalBWVmZVDN69GhotVoUFxdLNcePH4dWq31ufx5hYWE4ffo01Gq19AgODkZkZCTUajW8vLyYbQeNHTu2ye0VL1y4gP79+wPg7+2zuH//PszM9P/5Ym5uLp1BYradoytzHD16NMrKylBTUyPV5ObmwsrKCkFBQQY9TmNpbOYrKiqQn58PJycnvXFm+/SioqJw6tQpvdc0lUqFlStXYv/+/QCYa0dZWlpi5MiRrb6uMVsj6ppr7xFRcxpvW7djxw5x9uxZkZiYKOzs7MSlS5eMPbVu46233hIKhUIcOnRI1NTUSI/79+9LNRs3bhQKhUJkZWWJ06dPi3nz5jV7a6W+ffuK/Px8UVpaKiZMmNDsrVT8/f1FYWGhKCwsFH5+fs/VLara47dXuReC2XZUcXGxkMvl4v333xcVFRXi66+/Fra2tmL37t1SDbPtmJiYGNGnTx/ptnVZWVnC2dlZvPPOO1INs22fu3fvipMnT4qTJ08KAOKjjz4SJ0+elK603lU5Nt6mKiwsTJSWlor8/HzRt29fk75NVWvZ1tfXi+nTp4u+ffsKtVqt99pWV1cnbYPZNtXW7+yTnrzKvRDMtSVtZZuVlSUsLCzEtm3bREVFhfjb3/4mzM3NxZEjR6RtMFvjYENPZGSffvqp6N+/v7C0tBQjRoyQbsdGvwLQ7CMtLU2q0el0Yu3atUKpVAorKyvx0ksvidOnT+tt58GDByIuLk44OjoKGxsbMXXqVFFdXa1X88svv4jIyEhhb28v7O3tRWRkpLh9+3YXHGX38WRDz2w77vvvvxe+vr7CyspKeHt7i23btumNM9uOqa2tFQkJCcLd3V1YW1sLLy8vsXr1ar1GiNm2z8GDB5v9+xoTEyOE6NocL1++LKZMmSJsbGyEo6OjiIuLEw8fPjTk4RtUa9lWVVW1+Np28OBBaRvMtqm2fmef1FxDz1yb155sd+zYIQYOHCisra1FQECA2Lt3r942mK1xyIQQwrCfASAiIiIiIiKizsbv0BMRERERERGZIDb0RERERERERCaIDT0RERERERGRCWJDT0RERERERGSC2NATERERERERmSA29EREREREREQmiA09ERERERERkQliQ09ERERERERkgtjQExEREREREZkgNvREREREz4FLly5BJpNBrVYbeypERNRF2NATERERERERmSA29ERERESdQKfTISUlBQMHDoSVlRXc3d3x/vvvAwBOnz6NCRMmwMbGBk5OTvjzn/+Me/fuSeuOHz8eiYmJetubOXMmFixYID338PDABx98gIULF8Le3h7u7u7Ytm2bNO7p6QkACAwMhEwmw/jx4w12rERE1D2woSciIiLqBO+++y5SUlKwZs0anD17Ft988w3c3Nxw//59REREoFevXjhx4gQyMzORn5+PuLi4p97Hpk2bEBwcjJMnT2Lx4sV46623cO7cOQBAcXExACA/Px81NTXIysrq1OMjIqLuR27sCRARERGZurt37+KTTz5BamoqYmJiAAADBgzAuHHjsH37djx48AC7du2CnZ0dACA1NRXTpk1DSkoK3Nzc2r2fV155BYsXLwYAJCUl4eOPP8ahQ4fg7e0NFxcXAICTkxOUSmUnHyEREXVHPENPRERE9IzKy8tRV1eHsLCwZscCAgKkZh4Axo4dC51Oh/Pnzz/Vfvz9/aX/lslkUCqV0Gg0HZ84ERGZNDb0RERERM/IxsamxTEhBGQyWbNjjcvNzMwghNAbq6+vb1JvYWHRZH2dTve00yUioucEG3oiIiKiZzRo0CDY2Njgp59+ajI2dOhQqNVq/O9//5OWHT16FGZmZhg8eDAAwMXFBTU1NdJ4Q0MDysrKnmoOlpaW0rpERPT7wIaeiIiI6BlZW1sjKSkJ77zzDnbt2oXKykoUFRVhx44diIyMhLW1NWJiYlBWVoaDBw9i6dKliIqKkr4/P2HCBGRnZyM7Oxvnzp3D4sWLcefOnaeag6urK2xsbPDjjz/ixo0b0Gq1BjhSIiLqTtjQExEREXWCNWvWYMWKFfjrX/8KHx8fzJ07FxqNBra2tti/fz9u3bqFkSNHYs6cOQgLC0Nqaqq07sKFCxETE4Po6GiEhITA09MToaGhT7V/uVyOLVu2YOvWrVCpVJgxY0ZnHyIREXUzMvHkF7aIiIiIiIiIqNvjGXoiIiIiIiIiE8SGnoiIiIiIiMgEsaEnIiIiIiIiMkFs6ImIiIiIiIhMEBt6IiIiIiIiIhPEhp6IiIiIiIjIBLGhJyIiIiIiIjJBbOiJiIiIiIiITBAbeiIiIiIiIiITxIaeiIiIiIiIyASxoSciIiIiIiIyQf8HderSHb1IaMYAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "def perform_eda(df2018):\n", + " # Overview of the dataset\n", + " print(f'The dataset contains {df2018.shape[0]} rows and {df2018.shape[1]} columns.')\n", + " print(df2018.dtypes)\n", + " print(df2018.describe())\n", + " \n", + " # Missing values\n", + " missing_values = df2018.isnull().sum()\n", + " print(\"Missing values per column:\")\n", + " print(missing_values[missing_values > 0])\n", + " \n", + " # Unique values in categorical columns\n", + " for col in df2018.select_dtypes(include=['object']).columns:\n", + " print(f'Column {col} has {df2018[col].nunique()} unique values.')\n", + " print(df2018[col].value_counts().head(), '\\n')\n", + " \n", + " # Plot distributions of numeric columns\n", + " numeric_cols = df2018.select_dtypes(include=['int64', 'float64']).columns\n", + " df2018[numeric_cols].hist(figsize=(15, 10), bins=20, edgecolor='black')\n", + " plt.suptitle('Distributions of Numeric Columns')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.96])\n", + " plt.show()\n", + " \n", + " # Plot count plots for categorical columns\n", + " for col in df2018.select_dtypes(include=['object']).columns:\n", + " plt.figure(figsize=(10, 5))\n", + " sns.countplot(y=col, data=df2018, order=df2018[col].value_counts().index, palette='viridis')\n", + " plt.title(f'Count of {col}')\n", + " plt.show()\n", + " \n", + " # Correlation analysis\n", + " correlation_matrix = df2018.corr()\n", + " plt.figure(figsize=(12, 8))\n", + " sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n", + " plt.title('Correlation Matrix')\n", + " plt.show()\n", + " \n", + " # Outlier analysis using boxplots\n", + " for col in numeric_cols:\n", + " plt.figure(figsize=(10, 5))\n", + " sns.boxplot(x=df2018[col], palette='Set2')\n", + " plt.title(f'Boxplot of {col}')\n", + " plt.show()\n", + " \n", + " # Plot pie charts for categorical columns\n", + " for col in df2018.select_dtypes(include=['object']).columns:\n", + " plt.figure(figsize=(10, 5))\n", + " df2018[col].value_counts().plot.pie(autopct='%1.1f%%', startangle=140, colors=sns.color_palette('Set3'))\n", + " plt.title(f'Pie chart of {col}')\n", + " plt.ylabel('')\n", + " plt.show()\n", + " \n", + " return df2018\n", + "\n", + "# Perform EDA on the dataset\n", + "cleaned_df2018 = perform_eda(df2018)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(98855, 129)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2018.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#print(df2018.columns.tolist() !--> Listing coloumsn in table" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#dropping the columns\n", + "#drop_cols = ['Respondent', 'OpenSource', 'Student', 'FormalEducation', 'CompanySize', 'CareerSatisfaction', 'HopeFiveYears', 'LastNewJob', 'AssessJob1', 'AssessJob2', 'AssessJob3', 'AssessJob4', 'AssessJob5', 'AssessJob6', 'AssessJob7', 'AssessJob8', 'AssessJob9', 'AssessJob10', 'AssessBenefits1', 'AssessBenefits2', 'AssessBenefits3', 'AssessBenefits4', 'AssessBenefits5', 'AssessBenefits6', 'AssessBenefits7', 'AssessBenefits8', 'AssessBenefits9', 'AssessBenefits10', 'AssessBenefits11', 'JobContactPriorities1', 'JobContactPriorities2', 'JobContactPriorities3', 'JobContactPriorities4', 'JobContactPriorities5', 'JobEmailPriorities1', 'JobEmailPriorities2', 'JobEmailPriorities3', 'JobEmailPriorities4', 'JobEmailPriorities5', 'JobEmailPriorities6', 'JobEmailPriorities7', 'UpdateCV', 'CommunicationTools', 'TimeFullyProductive', 'EducationTypes', 'SelfTaughtTypes', 'TimeAfterBootcamp', 'HackathonReasons', 'AgreeDisagree1', 'AgreeDisagree2', 'AgreeDisagree3', 'DatabaseWorkedWith', 'DatabaseDesireNextYear', 'PlatformDesireNextYear', 'FrameworkWorkedWith', 'FrameworkDesireNextYear', 'IDE', 'NumberMonitors', 'Methodology', 'VersionControl', 'CheckInCode', 'AdBlocker', 'AdBlockerDisable', 'AdBlockerReasons', 'AdsAgreeDisagree1', 'AdsAgreeDisagree2', 'AdsAgreeDisagree3', 'AdsActions', 'AdsPriorities1', 'AdsPriorities2', 'AdsPriorities3', 'AdsPriorities4', 'AdsPriorities5', 'AdsPriorities6', 'AdsPriorities7', 'AIDangerous', 'AIInteresting', 'AIResponsible', 'AIFuture', 'EthicsChoice', 'EthicsReport', 'EthicsResponsible', 'EthicalImplications', 'StackOverflowRecommend', 'StackOverflowVisit', 'StackOverflowHasAccount', 'StackOverflowParticipate', 'StackOverflowJobs', 'StackOverflowDevStory', 'StackOverflowJobsRecommend', 'StackOverflowConsiderMember', 'HypotheticalTools1', 'HypotheticalTools2', 'HypotheticalTools3', 'HypotheticalTools4', 'HypotheticalTools5', 'WakeTime', 'HoursComputer', 'HoursOutside', 'SkipMeals', 'ErgonomicDevices', 'Exercise', 'SexualOrientation', 'EducationParents', 'Dependents', 'MilitaryUS', 'SurveyTooLong', 'SurveyEasy']\n", + "#df2018.drop(drop_cols, axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "#df2018.shape #checking rows and col after dropping the table" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Filtering - Sorting & Renaming\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "col=['Age','ConvertedSalary','Country','Currency','DevType','Employment','RaceEthnicity','Gender','SalaryType','Hobby','JobSatisfaction','JobSearchStatus','OperatingSystem','UndergradMajor','YearsCoding','YearsCodingProf','LanguageDesireNextYear','LanguageWorkedWith','FormalEducation']\n", + "df=df2018[col]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#renaming the colo\n", + "# 'ConvertedSalary': 'SalaryUSD'\n", + "df.rename(columns={'ConvertedSalary': 'SalaryUSD' }, inplace =True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeCountryCurrencyDevTypeEmploymentFormalEducationGenderHobbyJobSatisfactionJobSearchStatusLanguageDesireNextYearLanguageWorkedWithOperatingSystemRaceEthnicitySalaryTypeSalaryUSDUndergradMajorYearsCodingYearsCodingProf
025 - 34 years oldKenyaNaNFull-stack developerEmployed part-timeBachelor’s degree (BA, BS, B.Eng., etc.)MaleYesExtremely satisfiedI’m not actively looking, but I am open to new...JavaScript;Python;HTML;CSSJavaScript;Python;HTML;CSSLinux-basedBlack or of African descentMonthlyNaNMathematics or statistics3-5 years3-5 years
135 - 44 years oldUnited KingdomBritish pounds sterling (£)Database administrator;DevOps specialist;Full-...Employed full-timeBachelor’s degree (BA, BS, B.Eng., etc.)MaleYesModerately dissatisfiedI am actively looking for a jobGo;PythonJavaScript;Python;Bash/ShellLinux-basedWhite or of European descentYearly70841.0A natural science (ex. biology, chemistry, phy...30 or more years18-20 years
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" + ], + "text/plain": [ + " Age Country Currency \\\n", + "0 25 - 34 years old Kenya NaN \n", + "1 35 - 44 years old United Kingdom British pounds sterling (£) \n", + "\n", + " DevType Employment \\\n", + "0 Full-stack developer Employed part-time \n", + "1 Database administrator;DevOps specialist;Full-... Employed full-time \n", + "\n", + " FormalEducation Gender Hobby \\\n", + "0 Bachelor’s degree (BA, BS, B.Eng., etc.) Male Yes \n", + "1 Bachelor’s degree (BA, BS, B.Eng., etc.) Male Yes \n", + "\n", + " JobSatisfaction JobSearchStatus \\\n", + "0 Extremely satisfied I’m not actively looking, but I am open to new... \n", + "1 Moderately dissatisfied I am actively looking for a job \n", + "\n", + " LanguageDesireNextYear LanguageWorkedWith OperatingSystem \\\n", + "0 JavaScript;Python;HTML;CSS JavaScript;Python;HTML;CSS Linux-based \n", + "1 Go;Python JavaScript;Python;Bash/Shell Linux-based \n", + "\n", + " RaceEthnicity SalaryType SalaryUSD \\\n", + "0 Black or of African descent Monthly NaN \n", + "1 White or of European descent Yearly 70841.0 \n", + "\n", + " UndergradMajor YearsCoding \\\n", + "0 Mathematics or statistics 3-5 years \n", + "1 A natural science (ex. biology, chemistry, phy... 30 or more years \n", + "\n", + " YearsCodingProf \n", + "0 3-5 years \n", + "1 18-20 years " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.sort_index(axis=1).head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(98855, 19)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#21 col has been selected rfom 129, compared the shape\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Age 34281\n", + "SalaryUSD 51153\n", + "Country 412\n", + "Currency 36847\n", + "DevType 6757\n", + "Employment 3534\n", + "RaceEthnicity 41382\n", + "Gender 34386\n", + "SalaryType 47785\n", + "Hobby 0\n", + "JobSatisfaction 29579\n", + "JobSearchStatus 19367\n", + "OperatingSystem 22676\n", + "UndergradMajor 19819\n", + "YearsCoding 5020\n", + "YearsCodingProf 20952\n", + "LanguageDesireNextYear 25611\n", + "LanguageWorkedWith 20521\n", + "FormalEducation 4152\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "print(df.isnull().sum()) #Finding Null Values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Age object\n", + "SalaryUSD float64\n", + "Country object\n", + "Currency object\n", + "DevType object\n", + "Employment object\n", + "RaceEthnicity object\n", + "Gender object\n", + "SalaryType object\n", + "Hobby object\n", + "JobSatisfaction object\n", + "JobSearchStatus object\n", + "OperatingSystem object\n", + "UndergradMajor object\n", + "YearsCoding object\n", + "YearsCodingProf object\n", + "LanguageDesireNextYear object\n", + "LanguageWorkedWith object\n", + "FormalEducation object\n", + "dtype: object" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes #data_types" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Validation - Total Cells vs Missing %" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total : 1878245\n", + "Total missing : 424234\n", + "Missing Percentage: 22.58672324430519 %\n" + ] + } + ], + "source": [ + "#Find % of missing data\n", + "missing_count = df.isnull().sum() #number of missing\n", + "total_cells = np.product(df.shape) # number of cells (cols x rows)\n", + "total_missing = missing_count.sum()\n", + "missing_percent = (total_missing*100)/total_cells\n", + "\n", + "print('Total : ', total_cells)\n", + "print('Total missing : ', total_missing)\n", + "print('Missing Percentage: ', missing_percent, '%')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gender Filtering \n", + "#### Data Cleaning Starts" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Gender\n", + "Female 4025\n", + "Female;Male 98\n", + "Female;Male;Non-binary, genderqueer, or gender non-conforming 3\n", + "Female;Male;Transgender 14\n", + "Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming 50\n", + "Female;Non-binary, genderqueer, or gender non-conforming 50\n", + "Female;Transgender 145\n", + "Female;Transgender;Non-binary, genderqueer, or gender non-conforming 24\n", + "Male 59458\n", + "Male;Non-binary, genderqueer, or gender non-conforming 128\n", + "Male;Transgender 29\n", + "Male;Transgender;Non-binary, genderqueer, or gender non-conforming 5\n", + "Non-binary, genderqueer, or gender non-conforming 284\n", + "Transgender 105\n", + "Transgender;Non-binary, genderqueer, or gender non-conforming 51\n", + "Name: Gender, dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Gender: null = 13312 (21.6%)\n", + "df['Gender'].unique()\n", + "#count number of each gender\n", + "df.groupby('Gender')['Gender'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#replace\n", + "df['Gender'] = df['Gender'].fillna('Non-binary, genderqueer, or gender non-conforming')\n", + "df['Gender'].replace('Female;Male;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n", + "df['Gender'].replace('Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n", + "df['Gender'].replace('Female;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n", + "df['Gender'].replace('Female;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n", + "df['Gender'].replace('Male;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n", + "df['Gender'].replace('Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n", + "df['Gender'].replace('Transgender;Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) ##not sure\n", + "df['Gender'].replace('Female;Male', 'Female', inplace =True)\n", + "df['Gender'].replace('Female;Male;Transgender', 'Female', inplace =True)\n", + "df['Gender'].replace('Female;Transgender', 'Female', inplace =True)\n", + "df['Gender'].replace('Male;Transgender', 'Female', inplace =True) \n", + "df['Gender'].replace('Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) #\n", + "df['Gender'].replace('Transgender', 'Male', inplace =True) " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Gender\n", + "Female 4438\n", + "Male 59696\n", + "Non-conforming 34721\n", + "Name: Gender, dtype: int64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('Gender')['Gender'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(98855, 19)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()['Gender']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Country" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Country\n", + "Afghanistan 64\n", + "Albania 109\n", + "Algeria 130\n", + "Andorra 15\n", + "Angola 11\n", + " ... \n", + "Venezuela, Bolivarian Republic of... 123\n", + "Viet Nam 331\n", + "Yemen 13\n", + "Zambia 9\n", + "Zimbabwe 39\n", + "Name: Country, Length: 183, dtype: int64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('Country')['Country'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "412" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Country'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "df['Country'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Country'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hobby" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Hobby'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Hobby\n", + "No 18958\n", + "Yes 79897\n", + "Name: Hobby, dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('Hobby')['Hobby'].count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## undergrad" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "19819" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['UndergradMajor'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Computer science, computer engineering, or software engineering 50336\n", + "Another engineering discipline (ex. civil, electrical, mechanical) 6945\n", + "Information systems, information technology, or system administration 6507\n", + "A natural science (ex. biology, chemistry, physics) 3050\n", + "Mathematics or statistics 2818\n", + "Web development or web design 2418\n", + "A business discipline (ex. accounting, finance, marketing) 1921\n", + "A humanities discipline (ex. literature, history, philosophy) 1590\n", + "A social science (ex. anthropology, psychology, political science) 1377\n", + "Fine arts or performing arts (ex. graphic design, music, studio art) 1135\n", + "I never declared a major 693\n", + "A health science (ex. nursing, pharmacy, radiology) 246\n", + "Name: UndergradMajor, dtype: int64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['UndergradMajor'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def refactor_major(df):\n", + " conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'), \n", + " (df['UndergradMajor'] == 'Another engineering discipline (ex. civil, electrical, mechanical)'),\n", + " (df['UndergradMajor'] == 'Information systems, information technology, or system administration'), \n", + " (df['UndergradMajor'] == 'Mathematics or statistics'),\n", + " (df['UndergradMajor'] == 'A natural science (ex. biology, chemistry, physics)') \n", + " |(df['UndergradMajor'] == 'A health science (ex. nursing, pharmacy, radiology)'), \n", + " (df['UndergradMajor'] == 'Web development or web design'), \n", + " (df['UndergradMajor'] == 'A business discipline (ex. accounting, finance, marketing)'), \n", + " (df['UndergradMajor'] == 'A humanities discipline (ex. literature, history, philosophy)')\n", + " | (df['UndergradMajor'] == 'A social science (ex. anthropology, psychology, political science)')\n", + " | (df['UndergradMajor'] == 'Fine arts or performing arts (ex. graphic design, music, studio art)'),\n", + " (df['UndergradMajor'] == 'I never declared a major') ]\n", + " \n", + " choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'Other Science',\n", + " 'Web Design/Dev', 'Business', 'Arts and Science', 'No major']\n", + " df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n", + " return df\n", + "\n", + "df = refactor_major(df)\n", + "df['UndergradMajor'].replace('nan', 'No major', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Computer Science 50336\n", + "No major 20512\n", + "Engineering 6945\n", + "Info Systems 6507\n", + "Arts and Science 4102\n", + "Other Science 3296\n", + "Math/Stat 2818\n", + "Web Design/Dev 2418\n", + "Business 1921\n", + "Name: UndergradMajor, dtype: int64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['UndergradMajor'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['UndergradMajor'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "df.dropna(subset=['UndergradMajor'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['UndergradMajor'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Job Status" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "I’m not actively looking, but I am open to new opportunities 47556\n", + "I am not interested in new job opportunities 19296\n", + "I am actively looking for a job 12636\n", + "Name: JobSearchStatus, dtype: int64" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['JobSearchStatus'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "df.dropna(subset=['JobSearchStatus'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# refactoring JobStatus\n", + "# changing the jobstatus to seeking and non seeking\n", + "def refactor_job(df):\n", + " '''function to change JobStatus category to Seeking and Non Seeking'''\n", + " \n", + " conditions_job = [(df['JobSearchStatus'] == 'I am actively looking for a job'),\n", + " (df['JobSearchStatus'] == 'I am not interested in new job opportunities')\n", + " | (df['JobSearchStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n", + " \n", + " choices_job = ['Seeking', 'Not seeking']\n", + " \n", + " df['JobSearchStatus'] = np.select(conditions_job, choices_job, default=np.nan)\n", + " \n", + " return df\n", + "\n", + "df = refactor_job(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Not seeking 66852\n", + "Seeking 12636\n", + "Name: JobSearchStatus, dtype: int64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['JobSearchStatus'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['JobSearchStatus'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Employment" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Employed full-time 58551\n", + "Independent contractor, freelancer, or self-employed 7797\n", + "Not employed, but looking for work 4604\n", + "Employed part-time 4170\n", + "Not employed, and not looking for work 3210\n", + "Retired 138\n", + "Name: Employment, dtype: int64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Employment'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1018" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Employment'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "df['Employment'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "#im not considering the retired person here\n", + "#Refactoring the employment\n", + "def refactor_emp(df):\n", + " \n", + " conditions_emp = [(df['Employment'] == 'Employed full-time'),\n", + " (df['Employment'] == 'Independent contractor, freelancer, or self-employed'),\n", + " (df['Employment'] == 'Not employed, but looking for work'),\n", + " (df['Employment'] == 'Employed part-time')]\n", + " \n", + " choices_emp = ['Full-time', 'Self-employed', 'Not employed', 'Part-time']\n", + " \n", + " df['Employment'] = np.select(conditions_emp, choices_emp, default=np.nan)\n", + " \n", + " return df\n", + "\n", + "df = refactor_emp(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Full-time 58565\n", + "Self-employed 7825\n", + "Not employed 5161\n", + "Part-time 4179\n", + "nan 3758\n", + "Name: Employment, dtype: int64" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Employment'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Employment'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## JobSatisfaction" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Moderately satisfied 25908\n", + "Extremely satisfied 12395\n", + "Slightly satisfied 9973\n", + "Slightly dissatisfied 7037\n", + "Moderately dissatisfied 6286\n", + "Neither satisfied nor dissatisfied 4935\n", + "Extremely dissatisfied 2472\n", + "Name: JobSatisfaction, dtype: int64" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['JobSatisfaction'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10482" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['JobSatisfaction'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "df['JobSatisfaction'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['JobSatisfaction'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ethnicity" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "23578" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['RaceEthnicity'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RaceEthnicity\n", + "Black or of African descent 1204\n", + "Black or of African descent;East Asian 7\n", + "Black or of African descent;East Asian;Hispanic or Latino/Latina 2\n", + "Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian 1\n", + "Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian 1\n", + " ... \n", + "Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent 2\n", + "Native American, Pacific Islander, or Indigenous Australian;White or of European descent 160\n", + "South Asian 6112\n", + "South Asian;White or of European descent 88\n", + "White or of European descent 39320\n", + "Name: RaceEthnicity, Length: 71, dtype: int64" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#count number of each Ethnicity\n", + "df.groupby('RaceEthnicity')['RaceEthnicity'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n", + "df.loc[df['RaceEthnicity'].str.match('Biracial') == True, 'RaceEthnicity'] = 'Biracial'\n", + "df.loc[df['RaceEthnicity'].str.match('Black or of African descent') == True, 'RaceEthnicity'] = 'Black or African descent'\n", + "df.loc[df['RaceEthnicity'].str.match('East Asian') == True, 'RaceEthnicity'] = 'East Asian'\n", + "df.loc[df['RaceEthnicity'].str.match('Hispanic or Latino') == True, 'RaceEthnicity'] = 'Hispanic or Latino'\n", + "df.loc[df['RaceEthnicity'].str.match('Indigenous') == True, 'RaceEthnicity'] = 'Indigenous'\n", + "df.loc[df['RaceEthnicity'].str.match('Middle Eastern') == True, 'RaceEthnicity'] = 'Middle Eastern'\n", + "df.loc[df['RaceEthnicity'].str.match('South') == True, 'RaceEthnicity'] = 'South Asian'\n", + "df.loc[df['RaceEthnicity'].str.match('White or of European descent') == True, 'RaceEthnicity'] = 'White or European descent'\n", + "df.loc[df['RaceEthnicity'].str.match('Multiracial') == True, 'RaceEthnicity'] = 'Multiracial'\n", + "df.loc[df['RaceEthnicity'].str.match('Native American') == True, 'RaceEthnicity'] = 'Native American'" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RaceEthnicity\n", + "Black or African descent 1549\n", + "East Asian 2787\n", + "Hispanic or Latino 3592\n", + "Middle Eastern 2176\n", + "Native American 286\n", + "South Asian 6200\n", + "White or European descent 39320\n", + "Name: RaceEthnicity, dtype: int64" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('RaceEthnicity')['RaceEthnicity'].count() #11 groups of Ethnicity after combining" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "23578" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['RaceEthnicity'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "df['RaceEthnicity']=df.groupby(['Country'])['RaceEthnicity'].bfill().ffill()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['RaceEthnicity'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Developer Roles" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "728" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['DevType'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "df['DevType'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DevType\n", + "Back-end developer 5372\n", + "Back-end developer;C-suite executive (CEO, CTO, etc.) 59\n", + "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst 5\n", + "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst;Data scientist or machine learning specialist 1\n", + "Back-end developer;C-suite executive (CEO, CTO, etc.);Data or business analyst;Data scientist or machine learning specialist;Database administrator;Designer;Desktop or enterprise applications developer 1\n", + " ... \n", + "QA or test developer;Student;System administrator 5\n", + "QA or test developer;System administrator 10\n", + "Student 2523\n", + "Student;System administrator 63\n", + "System administrator 247\n", + "Name: DevType, Length: 8820, dtype: int64" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('DevType')['DevType'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n", + "df.loc[df['DevType'].str.match('Back-end developer') == True, 'DevType'] = 'Developer'\n", + "df.loc[df['DevType'].str.match('Student') == True, 'DevType'] = 'Student'\n", + "df.loc[df['DevType'].str.match('QA or test developer') == True, 'DevType'] = 'Non developer'\n", + "df.loc[df['DevType'].str.match('Product manager') == True, 'DevType'] = 'Manager'\n", + "df.loc[df['DevType'].str.match('Mobile developer') == True, 'DevType'] = 'Developer'\n", + "df.loc[df['DevType'].str.match('Marketing or sales professional') == True, 'DevType'] = 'Non developer'\n", + "\n", + "df.loc[df['DevType'].str.match('System administrator') == True, 'DevType'] = 'Developer'\n", + "df.loc[df['DevType'].str.match('Game or graphics developer') == True, 'DevType'] = 'Developer'\n", + "df.loc[df['DevType'].str.match('Full-stack developer') == True, 'DevType'] = 'Developer'\n", + "df.loc[df['DevType'].str.match('Front-end developer') == True, 'DevType'] = 'Developer'\n", + "df.loc[df['DevType'].str.match('Engineering manager') == True, 'DevType'] = 'Manager'\n", + "df.loc[df['DevType'].str.match('Embedded applications or devices developer') == True, 'DevType'] = 'Developer'\n", + "df.loc[df['DevType'].str.match('Educator or academic researcher') == True, 'DevType'] = 'Student'\n", + "df.loc[df['DevType'].str.match('DevOps specialist') == True, 'DevType'] = 'Developer'\n", + "df.loc[df['DevType'].str.match('Desktop or enterprise applications developer') == True, 'DevType'] = 'Developer'\n", + "\n", + "df.loc[df['DevType'].str.match('Designer') == True, 'DevType'] = 'Non developer'\n", + "df.loc[df['DevType'].str.match('Database administrator') == True, 'DevType'] = 'Developer'\n", + "df.loc[df['DevType'].str.match('Data scientist or machine learning specialist') == True, 'DevType'] = 'Developer'\n", + "df.loc[df['DevType'].str.match('Data or business analyst') == True, 'DevType'] = 'Developer'\n", + "df.loc[df['DevType'].str.match('C-suite executive') == True, 'DevType'] = 'Developer'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DevType\n", + "Developer 73032\n", + "Manager 665\n", + "Non developer 2791\n", + "Student 3000\n", + "Name: DevType, dtype: int64" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby('DevType')['DevType'].count() #11 groups of Ethnicity after combining" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "#df['Salary'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Language to worked with" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "C#;JavaScript;SQL;HTML;CSS 1235\n", + "JavaScript;PHP;SQL;HTML;CSS 1095\n", + "Java 855\n", + "Name: LanguageWorkedWith, dtype: int64" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['LanguageWorkedWith'].value_counts().nlargest(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9985" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['LanguageWorkedWith'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "df['LanguageWorkedWith'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['LanguageWorkedWith'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "C#;JavaScript;SQL;HTML;CSS 1383\n", + "JavaScript;PHP;SQL;HTML;CSS 1226\n", + "Java 989\n", + "Name: LanguageWorkedWith, dtype: int64" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['LanguageWorkedWith'].value_counts().nlargest(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LanguageDesireNextYear" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 718\n", + "C#;JavaScript;SQL;TypeScript;HTML;CSS 557\n", + "C# 522\n", + "Name: LanguageDesireNextYear, dtype: int64" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['LanguageDesireNextYear'].value_counts().nlargest(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14147" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['LanguageDesireNextYear'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "df['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['LanguageDesireNextYear'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 878\n", + "C#;JavaScript;SQL;TypeScript;HTML;CSS 690\n", + "C# 629\n", + "Name: LanguageDesireNextYear, dtype: int64" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['LanguageDesireNextYear'].value_counts().nlargest(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Years of coding (Exp)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0-2 years 22612\n", + "3-5 years 20883\n", + "6-8 years 11177\n", + "9-11 years 7456\n", + "12-14 years 4220\n", + "15-17 years 2987\n", + "18-20 years 2810\n", + "21-23 years 1352\n", + "30 or more years 1289\n", + "24-26 years 853\n", + "Name: YearsCodingProf, dtype: int64" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['YearsCodingProf'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3349" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['YearsCodingProf'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "df['YearsCodingProf'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['YearsCodingProf'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3-5 years 23773\n", + "0-2 years 22781\n", + "6-8 years 11274\n", + "9-11 years 7527\n", + "12-14 years 4267\n", + "15-17 years 3007\n", + "18-20 years 2841\n", + "21-23 years 1365\n", + "30 or more years 1294\n", + "24-26 years 856\n", + "Name: YearsCodingProf, dtype: int64" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['YearsCodingProf'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Years of coding (Non Exp)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3-5 years 19100\n", + "6-8 years 16537\n", + "9-11 years 10578\n", + "0-2 years 8022\n", + "12-14 years 7069\n", + "15-17 years 5459\n", + "18-20 years 4472\n", + "30 or more years 3136\n", + "21-23 years 2377\n", + "24-26 years 1671\n", + "Name: YearsCoding, dtype: int64" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['YearsCoding'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "105" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['YearsCoding'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "df['YearsCoding'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['YearsCoding'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3-5 years 19135\n", + "6-8 years 16554\n", + "9-11 years 10585\n", + "0-2 years 8043\n", + "12-14 years 7077\n", + "15-17 years 5462\n", + "18-20 years 4476\n", + "30 or more years 3144\n", + "21-23 years 2378\n", + "24-26 years 1671\n", + "Name: YearsCoding, dtype: int64" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['YearsCoding'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Operating System" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Windows 34268\n", + "MacOS 18638\n", + "Linux-based 16069\n", + "BSD/Unix 139\n", + "Name: OperatingSystem, dtype: int64" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['OperatingSystem'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10374" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['OperatingSystem'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "df['OperatingSystem'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['OperatingSystem'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Windows 39475\n", + "MacOS 21433\n", + "Linux-based 18406\n", + "BSD/Unix 174\n", + "Name: OperatingSystem, dtype: int64" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['OperatingSystem'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Salary Type" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Monthly 26201\n", + "Yearly 22541\n", + "Weekly 2248\n", + "Name: SalaryType, dtype: int64" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['SalaryType'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "28498" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['SalaryType'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "df['SalaryType'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['SalaryType'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Monthly 40953\n", + "Yearly 34333\n", + "Weekly 4202\n", + "Name: SalaryType, dtype: int64" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['SalaryType'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Currency" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "U.S. dollars ($) 20599\n", + "Euros (€) 15201\n", + "Indian rupees (₹) 7908\n", + "British pounds sterling (£) 4856\n", + "Canadian dollars (C$) 2535\n", + "Russian rubles (₽) 1768\n", + "Brazilian reais (R$) 1663\n", + "Australian dollars (A$) 1571\n", + "Polish złoty (zł) 1434\n", + "Swedish kroner (SEK) 864\n", + "Name: Currency, dtype: int64" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Currency'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "17483" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Currency'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "df['Currency'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Currency'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [], + "source": [ + "df.dropna(subset=['Currency'], inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "U.S. dollars ($) 26356\n", + "Euros (€) 19465\n", + "Indian rupees (₹) 10152\n", + "British pounds sterling (£) 6194\n", + "Canadian dollars (C$) 3289\n", + "Russian rubles (₽) 2340\n", + "Brazilian reais (R$) 2122\n", + "Australian dollars (A$) 1970\n", + "Polish złoty (zł) 1856\n", + "Swedish kroner (SEK) 1101\n", + "Name: Currency, dtype: int64" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Currency'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Salary" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0 842\n", + "120000.0 524\n", + "100000.0 497\n", + "80000.0 396\n", + "1000000.0 382\n", + "110000.0 371\n", + "90000.0 364\n", + "150000.0 357\n", + "60000.0 351\n", + "75000.0 337\n", + "Name: SalaryUSD, dtype: int64" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['SalaryUSD'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "31786" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['SalaryUSD'].isnull().sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DevType Country \n", + "Student Saudi Arabia 1500000.0\n", + "Developer Andorra 525089.5\n", + "Manager Hungary 516000.0\n", + " Netherlands 507175.0\n", + "Non developer Algeria 360000.0\n", + " Cyprus 293736.0\n", + "Developer Liechtenstein 284028.0\n", + "Student Finland 272212.0\n", + "Manager Denmark 262920.6\n", + "Student Israel 256522.4\n", + "Name: SalaryUSD, dtype: float64" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_salary = df.groupby(['DevType','Country'])['SalaryUSD'].mean() \n", + "mean_salary.nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + "source": [ + "means = df.groupby(['YearsCodingProf','DevType', 'Country'])['SalaryUSD'].transform('mean')\n", + "df['SalaryUSD'] = df['SalaryUSD'].fillna(means)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "YearsCodingProf DevType Country \n", + "9-11 years Student Saudi Arabia 1500000.0\n", + "12-14 years Non developer Norway 1000000.0\n", + " Student Switzerland 1000000.0\n", + "15-17 years Non developer Australia 1000000.0\n", + " New Zealand 1000000.0\n", + "21-23 years Developer Japan 1000000.0\n", + " Venezuela, Bolivarian Republic of... 1000000.0\n", + " Non developer Sweden 1000000.0\n", + " Student Finland 1000000.0\n", + "24-26 years Manager Canada 1000000.0\n", + "Name: SalaryUSD, dtype: float64" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_salary = df.groupby(['YearsCodingProf','DevType','Country'])['SalaryUSD'].mean()\n", + "mean_salary.nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "df.dropna(subset=['SalaryUSD'], inplace = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Age" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25 - 34 years old 30969\n", + "18 - 24 years old 14847\n", + "35 - 44 years old 10980\n", + "45 - 54 years old 3072\n", + "Under 18 years old 1549\n", + "55 - 64 years old 865\n", + "65 years or older 144\n", + "Name: Age, dtype: int64" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Age'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "16374" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Age'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "df['Age'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Age'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25 - 34 years old 38797\n", + "18 - 24 years old 19074\n", + "35 - 44 years old 13665\n", + "45 - 54 years old 3821\n", + "Under 18 years old 2165\n", + "55 - 64 years old 1093\n", + "65 years or older 185\n", + "Name: Age, dtype: int64" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Age'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Age 0\n", + "SalaryUSD 0\n", + "Country 0\n", + "Currency 0\n", + "DevType 0\n", + "Employment 0\n", + "RaceEthnicity 0\n", + "Gender 0\n", + "SalaryType 0\n", + "Hobby 0\n", + "JobSatisfaction 0\n", + "JobSearchStatus 0\n", + "OperatingSystem 0\n", + "UndergradMajor 0\n", + "YearsCoding 0\n", + "YearsCodingProf 0\n", + "LanguageDesireNextYear 0\n", + "LanguageWorkedWith 0\n", + "FormalEducation 1549\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "print(df.isnull().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1549" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['FormalEducation'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Bachelor’s degree (BA, BS, B.Eng., etc.) 36010\n", + "Master’s degree (MA, MS, M.Eng., MBA, etc.) 17529\n", + "Some college/university study without earning a degree 9737\n", + "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.) 7088\n", + "Associate degree 2407\n", + "Other doctoral degree (Ph.D, Ed.D., etc.) 1754\n", + "Primary/elementary school 1217\n", + "Professional degree (JD, MD, etc.) 1073\n", + "I never completed any formal education 436\n", + "Name: FormalEducation, dtype: int64" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Mona2018\n", + "df['FormalEducation'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Bachelors 37559\n", + "No Degree 18478\n", + "Masters 17529\n", + "Associate 2407\n", + "Doctorate 1754\n", + "Professional 1073\n", + "Name: EdLevel, dtype: int64" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Mona2018\n", + "#Changing column's name\n", + "df.rename(columns={'FormalEducation':'EdLevel'}, inplace =True)\n", + "#Refactoring EdLevel\n", + "def refactor_ed(df):\n", + " '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n", + " conditions_ed = [(df['EdLevel'] == 'Associate degree'),\n", + " (df['EdLevel'] == 'Bachelor’s degree (BA, BS, B.Eng., etc.)'),\n", + " (df['EdLevel'] == 'Master’s degree (MA, MS, M.Eng., MBA, etc.)'),\n", + " (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'), \n", + " (df['EdLevel'] == 'Other doctoral degree (Ph.D, Ed.D., etc.)'),\n", + " (df['EdLevel'] == 'Some college/university study without earning a degree') \n", + " | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n", + " | (df['EdLevel'] == 'Primary/elementary school')\n", + " | (df['EdLevel'] == 'I never completed any formal education')]\n", + " \n", + " choices_ed = ['Associate', 'Bachelors', 'Masters', 'Professional', 'Doctorate', 'No Degree']\n", + " df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n", + " return df\n", + "\n", + "# applying function to subsets\n", + "df = refactor_ed(df)\n", + "#Assigining the surveyors who havent mentioned their education level to Bachelor’s degree\n", + "df['EdLevel'].replace('nan', 'Bachelors', inplace=True)\n", + "\n", + "df['EdLevel'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleaned Dataset : 2018_Survey" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(78800, 19)" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cleaned_2018 = df[df.notnull()]\n", + "cleaned_2018.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeSalaryUSDCountryCurrencyDevTypeEmploymentRaceEthnicityGenderSalaryTypeHobbyJobSatisfactionJobSearchStatusOperatingSystemUndergradMajorYearsCodingYearsCodingProfLanguageDesireNextYearLanguageWorkedWithEdLevel
135 - 44 years old70841.000000United KingdomBritish pounds sterling (£)DeveloperFull-timeWhite or European descentMaleYearlyYesModerately dissatisfiedSeekingLinux-basedOther Science30 or more years18-20 yearsGo;PythonJavaScript;Python;Bash/ShellBachelors
235 - 44 years old153030.333333United StatesBritish pounds sterling (£)ManagerFull-timeWhite or European descentNon-conformingYearlyYesModerately satisfiedNot seekingLinux-basedComputer Science24-26 years6-8 yearsGo;PythonJavaScript;Python;Bash/ShellAssociate
335 - 44 years old165809.207657United StatesU.S. dollars ($)DeveloperFull-timeWhite or European descentMaleYearlyNoNeither satisfied nor dissatisfiedNot seekingWindowsComputer Science18-20 years12-14 yearsC#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/ShellC#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/ShellBachelors
418 - 24 years old21426.000000South AfricaSouth African rands (R)DeveloperFull-timeWhite or European descentMaleYearlyYesSlightly satisfiedNot seekingWindowsComputer Science6-8 years0-2 yearsAssembly;C;C++;Matlab;SQL;Bash/ShellC;C++;Java;Matlab;R;SQL;Bash/ShellNo Degree
518 - 24 years old41671.000000United KingdomBritish pounds sterling (£)DeveloperFull-timeWhite or European descentMaleYearlyYesModerately satisfiedSeekingLinux-basedComputer Science6-8 years3-5 yearsC#;Go;Java;JavaScript;Python;SQL;TypeScript;HT...Java;JavaScript;Python;TypeScript;HTML;CSSBachelors
618 - 24 years old120000.000000United StatesU.S. dollars ($)DeveloperFull-timeWhite or European descentMaleYearlyYesSlightly satisfiedNot seekingMacOSComputer Science9-11 years0-2 yearsC;Go;JavaScript;Python;HTML;CSSJavaScript;HTML;CSSNo Degree
725 - 34 years old93336.000000NigeriaU.S. dollars ($)Non developerFull-timeBlack or African descentFemaleYearlyYesSlightly satisfiedNot seekingWindowsComputer Science0-2 years3-5 yearsMatlab;SQL;Kotlin;Bash/ShellJavaScript;TypeScript;HTML;CSSBachelors
835 - 44 years old250000.000000United StatesU.S. dollars ($)DeveloperFull-timeWhite or European descentMaleYearlyYesModerately satisfiedNot seekingMacOSArts and Science30 or more years21-23 yearsErlang;Go;Python;Rust;SQLAssembly;CoffeeScript;Erlang;Go;JavaScript;Lua...No Degree
1335 - 44 years old26023.003365IndiaU.S. dollars ($)DeveloperFull-timeSouth AsianNon-conformingYearlyNoExtremely satisfiedNot seekingLinux-basedEngineering3-5 years3-5 yearsJava;PythonJavaBachelors
1418 - 24 years old0.000000NetherlandsEuros (€)DeveloperFull-timeWhite or European descentMaleMonthlyNoNeither satisfied nor dissatisfiedNot seekingWindowsNo major0-2 years0-2 yearsJava;PythonJava;JavaScript;PHP;VB.NET;HTML;CSSNo Degree
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" + ], + "text/plain": [ + " Age SalaryUSD Country \\\n", + "1 35 - 44 years old 70841.000000 United Kingdom \n", + "2 35 - 44 years old 153030.333333 United States \n", + "3 35 - 44 years old 165809.207657 United States \n", + "4 18 - 24 years old 21426.000000 South Africa \n", + "5 18 - 24 years old 41671.000000 United Kingdom \n", + "6 18 - 24 years old 120000.000000 United States \n", + "7 25 - 34 years old 93336.000000 Nigeria \n", + "8 35 - 44 years old 250000.000000 United States \n", + "13 35 - 44 years old 26023.003365 India \n", + "14 18 - 24 years old 0.000000 Netherlands \n", + "\n", + " Currency DevType Employment \\\n", + "1 British pounds sterling (£) Developer Full-time \n", + "2 British pounds sterling (£) Manager Full-time \n", + "3 U.S. dollars ($) Developer Full-time \n", + "4 South African rands (R) Developer Full-time \n", + "5 British pounds sterling (£) Developer Full-time \n", + "6 U.S. dollars ($) Developer Full-time \n", + "7 U.S. dollars ($) Non developer Full-time \n", + "8 U.S. dollars ($) Developer Full-time \n", + "13 U.S. dollars ($) Developer Full-time \n", + "14 Euros (€) Developer Full-time \n", + "\n", + " RaceEthnicity Gender SalaryType Hobby \\\n", + "1 White or European descent Male Yearly Yes \n", + "2 White or European descent Non-conforming Yearly Yes \n", + "3 White or European descent Male Yearly No \n", + "4 White or European descent Male Yearly Yes \n", + "5 White or European descent Male Yearly Yes \n", + "6 White or European descent Male Yearly Yes \n", + "7 Black or African descent Female Yearly Yes \n", + "8 White or European descent Male Yearly Yes \n", + "13 South Asian Non-conforming Yearly No \n", + "14 White or European descent Male Monthly No \n", + "\n", + " JobSatisfaction JobSearchStatus OperatingSystem \\\n", + "1 Moderately dissatisfied Seeking Linux-based \n", + "2 Moderately satisfied Not seeking Linux-based \n", + "3 Neither satisfied nor dissatisfied Not seeking Windows \n", + "4 Slightly satisfied Not seeking Windows \n", + "5 Moderately satisfied Seeking Linux-based \n", + "6 Slightly satisfied Not seeking MacOS \n", + "7 Slightly satisfied Not seeking Windows \n", + "8 Moderately satisfied Not seeking MacOS \n", + "13 Extremely satisfied Not seeking Linux-based \n", + "14 Neither satisfied nor dissatisfied Not seeking Windows \n", + "\n", + " UndergradMajor YearsCoding YearsCodingProf \\\n", + "1 Other Science 30 or more years 18-20 years \n", + "2 Computer Science 24-26 years 6-8 years \n", + "3 Computer Science 18-20 years 12-14 years \n", + "4 Computer Science 6-8 years 0-2 years \n", + "5 Computer Science 6-8 years 3-5 years \n", + "6 Computer Science 9-11 years 0-2 years \n", + "7 Computer Science 0-2 years 3-5 years \n", + "8 Arts and Science 30 or more years 21-23 years \n", + "13 Engineering 3-5 years 3-5 years \n", + "14 No major 0-2 years 0-2 years \n", + "\n", + " LanguageDesireNextYear \\\n", + "1 Go;Python \n", + "2 Go;Python \n", + "3 C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell \n", + "4 Assembly;C;C++;Matlab;SQL;Bash/Shell \n", + "5 C#;Go;Java;JavaScript;Python;SQL;TypeScript;HT... \n", + "6 C;Go;JavaScript;Python;HTML;CSS \n", + "7 Matlab;SQL;Kotlin;Bash/Shell \n", + "8 Erlang;Go;Python;Rust;SQL \n", + "13 Java;Python \n", + "14 Java;Python \n", + "\n", + " LanguageWorkedWith EdLevel \n", + "1 JavaScript;Python;Bash/Shell Bachelors \n", + "2 JavaScript;Python;Bash/Shell Associate \n", + "3 C#;JavaScript;SQL;TypeScript;HTML;CSS;Bash/Shell Bachelors \n", + "4 C;C++;Java;Matlab;R;SQL;Bash/Shell No Degree \n", + "5 Java;JavaScript;Python;TypeScript;HTML;CSS Bachelors \n", + "6 JavaScript;HTML;CSS No Degree \n", + "7 JavaScript;TypeScript;HTML;CSS Bachelors \n", + "8 Assembly;CoffeeScript;Erlang;Go;JavaScript;Lua... No Degree \n", + "13 Java Bachelors \n", + "14 Java;JavaScript;PHP;VB.NET;HTML;CSS No Degree " + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cleaned_2018.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## After Cleaning Data" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total : 1497200\n", + "Total missing : 0\n", + "Missing Percentage: 0.0 %\n" + ] + } + ], + "source": [ + "#Find % of missing data\n", + "missing_count = df.isnull().sum() #number of missing\n", + "total_cells = np.product(df.shape) # number of cells (cols x rows)\n", + "total_missing = missing_count.sum()\n", + "missing_percent = (total_missing*100)/total_cells\n", + "\n", + "print('Total : ', total_cells)\n", + "print('Total missing : ', total_missing)\n", + "print('Missing Percentage: ', missing_percent, '%')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stackoverflow 2019 Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "na_vals = ['NA', 'Missing']\n", + "survey_main_df = pd.read_csv(r'C:\\Users\\sanja\\Stack_Data\\survey_results_public_2019.csv', na_values=na_vals)\n", + "schema_df = pd.read_csv(r'C:\\Users\\sanja\\Stack_Data\\survey_results_public_2019.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Cleaning" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [], + "source": [ + "#Selecting only the required columns for analysis\n", + "survey_df_2019 = survey_main_df[['Age', 'CareerSat', 'ConvertedComp', 'Country', 'Dependents', 'EdLevel', 'Employment', 'Ethnicity', 'Gender', 'Hobbyist', 'ImpSyn', 'JobSat', 'JobSeek', 'LanguageDesireNextYear', 'LanguageWorkedWith', 'MainBranch',\n", + " 'UndergradMajor', 'YearsCodePro', 'DevType']]" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#changing the name of columns for easier understanding\n", + "# 'MainBranch': 'Profession'\n", + "# 'ConvertedComp': 'SalaryUSD'\n", + "# 'CareerSat': 'JobSatisfaction'\n", + "# 'ImpSyn' : 'CompetenceLevel'\n", + "# 'JobSat' : 'CurrentJobSatis'\n", + "# 'JobSeek' : 'JobStatus'\n", + "\n", + "\n", + "survey_df_2019.rename(columns={'MainBranch': 'Profession', 'ConvertedComp': 'SalaryUSD', 'CareerSat': 'JobSatisfaction', 'ImpSyn' : 'CompetenceLevel', 'JobSat' : 'CurrentJobSatis', 'JobSeek' : 'JobStatus' }, inplace =True)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeCompetenceLevelCountryCurrentJobSatisDependentsDevTypeEdLevelEmploymentEthnicityGenderHobbyistJobSatisfactionJobStatusLanguageDesireNextYearLanguageWorkedWithProfessionSalaryUSDUndergradMajorYearsCodePro
014.0NaNUnited KingdomNaNNoNaNPrimary/elementary schoolNot employed, and not looking for workNaNManYesNaNNaNC;C++;C#;Go;HTML/CSS;Java;JavaScript;Python;SQLHTML/CSS;Java;JavaScript;PythonI am a student who is learning to codeNaNNaNNaN
119.0NaNBosnia and HerzegovinaNaNNoDeveloper, desktop or enterprise applications;...Secondary school (e.g. American high school, G...Not employed, but looking for workNaNManNoNaNI am actively looking for a jobC++;HTML/CSS;JavaScript;SQLC++;HTML/CSS;PythonI am a student who is learning to codeNaNNaNNaN
228.0AverageThailandSlightly satisfiedYesDesigner;Developer, back-end;Developer, front-...Bachelor’s degree (BA, BS, B.Eng., etc.)Employed full-timeNaNManYesSlightly satisfiedI’m not actively looking, but I am open to new...Elixir;HTML/CSSHTML/CSSI am not primarily a developer, but I write co...8820.0Web development or web design1
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" + ], + "text/plain": [ + " Age CompetenceLevel Country CurrentJobSatis \\\n", + "0 14.0 NaN United Kingdom NaN \n", + "1 19.0 NaN Bosnia and Herzegovina NaN \n", + "2 28.0 Average Thailand Slightly satisfied \n", + "\n", + " Dependents DevType \\\n", + "0 No NaN \n", + "1 No Developer, desktop or enterprise applications;... \n", + "2 Yes Designer;Developer, back-end;Developer, front-... \n", + "\n", + " EdLevel \\\n", + "0 Primary/elementary school \n", + "1 Secondary school (e.g. American high school, G... \n", + "2 Bachelor’s degree (BA, BS, B.Eng., etc.) \n", + "\n", + " Employment Ethnicity Gender Hobbyist \\\n", + "0 Not employed, and not looking for work NaN Man Yes \n", + "1 Not employed, but looking for work NaN Man No \n", + "2 Employed full-time NaN Man Yes \n", + "\n", + " JobSatisfaction JobStatus \\\n", + "0 NaN NaN \n", + "1 NaN I am actively looking for a job \n", + "2 Slightly satisfied I’m not actively looking, but I am open to new... \n", + "\n", + " LanguageDesireNextYear \\\n", + "0 C;C++;C#;Go;HTML/CSS;Java;JavaScript;Python;SQL \n", + "1 C++;HTML/CSS;JavaScript;SQL \n", + "2 Elixir;HTML/CSS \n", + "\n", + " LanguageWorkedWith \\\n", + "0 HTML/CSS;Java;JavaScript;Python \n", + "1 C++;HTML/CSS;Python \n", + "2 HTML/CSS \n", + "\n", + " Profession SalaryUSD \\\n", + "0 I am a student who is learning to code NaN \n", + "1 I am a student who is learning to code NaN \n", + "2 I am not primarily a developer, but I write co... 8820.0 \n", + "\n", + " UndergradMajor YearsCodePro \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 Web development or web design 1 " + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#sorting the columns alphabetically\n", + "survey_df_2019.sort_index(axis=1).head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Age float64\n", + "JobSatisfaction object\n", + "SalaryUSD float64\n", + "Country object\n", + "Dependents object\n", + "EdLevel object\n", + "Employment object\n", + "Ethnicity object\n", + "Gender object\n", + "Hobbyist object\n", + "CompetenceLevel object\n", + "CurrentJobSatis object\n", + "JobStatus object\n", + "LanguageDesireNextYear object\n", + "LanguageWorkedWith object\n", + "Profession object\n", + "UndergradMajor object\n", + "YearsCodePro object\n", + "DevType object\n", + "dtype: object" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#datatype of survey data\n", + "survey_df_2019.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleaning and Refactoring column values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gender" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Man 77919\n", + "Woman 6344\n", + "Non-binary, genderqueer, or gender non-conforming 597\n", + "Man;Non-binary, genderqueer, or gender non-conforming 181\n", + "Woman;Non-binary, genderqueer, or gender non-conforming 163\n", + "Woman;Man 132\n", + "Woman;Man;Non-binary, genderqueer, or gender non-conforming 70\n", + "Name: Gender, dtype: int64" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Gender'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "#lets refactor Gender values to Male, female and Non binary\n", + "#For the purpose of our data analysis we are considering three gender category. This not to defame any gender.\n", + "#refactoring Gender\n", + "\n", + "def refactor_gender(df):\n", + " '''function to change gender category to Male, Female, Non binary'''\n", + " conditions = [(df['Gender'] == 'Man') | (df['Gender'] == 'Man;Non-binary, genderqueer, or gender non-conforming'),\n", + " (df['Gender'] == 'Woman') | (df['Gender'] == 'Woman;Non-binary, genderqueer, or gender non-conforming'),\n", + " (df['Gender'] == 'Non-binary, genderqueer, or gender non-conforming') \n", + " | (df['Gender'] == 'Woman;Man') \n", + " | (df['Gender'] == 'Woman;Man;Non-binary, genderqueer, or gender non-conforming')]\n", + "\n", + " values = ['Man', 'Woman', 'Non-binary']\n", + "\n", + " df['Gender'] = np.select(conditions, values, default = np.NaN)\n", + " \n", + " return df\n", + " \n", + "survey_df_2019 = refactor_gender(survey_df_2019)\n", + "survey_df_2019['Gender'].replace('nan', 'Non-binary', inplace =True)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019['Gender'] = survey_df_2019['Gender'].fillna('Non-binary')" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Assigining the surveyors who havent mentioned their gender to Non-Binary category\n", + "survey_df_2019.isnull().sum()['Gender']" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Gender\n", + "Man 78100\n", + "Non-binary 4276\n", + "Woman 6507\n", + "Name: Gender, dtype: int64" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019.groupby('Gender')['Gender'].count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Age" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(x='Age', y= 'Gender', data=survey_df_2019)\n", + "plt.title(\"Before cleaning Age's outliers from genders\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "#We are considering developes of age 15 to 60\n", + "filt = (survey_df_2019['Age'] >= 15) & (survey_df_2019['Age'] <= 60)\n", + "survey_df_2019 = survey_df_2019[filt]" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(x='Age', y= 'Gender', data=survey_df_2019)\n", + "plt.title(\"After cleaning Age's outliers from genders\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Age'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Profession column (Mainbranch)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "I am a developer by profession 59247\n", + "I am a student who is learning to code 8382\n", + "I am not primarily a developer, but I write code sometimes as part of my work 6531\n", + "I code primarily as a hobby 2370\n", + "I used to be a developer by profession, but no longer am 1210\n", + "Name: Profession, dtype: int64" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Profession'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "255" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Profession'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019['Profession'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "I am a developer by profession 59502\n", + "I am a student who is learning to code 8382\n", + "I am not primarily a developer, but I write code sometimes as part of my work 6531\n", + "I code primarily as a hobby 2370\n", + "I used to be a developer by profession, but no longer am 1210\n", + "Name: Profession, dtype: int64" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Profession'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#Lets refactor column values of Profession column\n", + "#refactoring profession column\n", + "\n", + "def refactor_prof(df):\n", + " '''function to change Profession category to Developer, Student, Non-Developer, Novoice, Ex-Developer'''\n", + " conditions_prof = [(df['Profession'] == 'I am a developer by profession'),\n", + " (df['Profession'] == 'I am a student who is learning to code'),\n", + " (df['Profession'] == 'I am not primarily a developer, but I write code sometimes as part of my work'),\n", + " (df['Profession'] == 'I code primarily as a hobby'),\n", + " (df['Profession'] == 'I used to be a developer by profession, but no longer am')]\n", + " \n", + " choices_prof = ['Developer', 'Student', 'Non developer', 'Novoice', 'Ex-Developer']\n", + " \n", + " df['Profession'] = np.select(conditions_prof, choices_prof, default=np.nan)\n", + " \n", + " return df\n", + "\n", + "survey_df_2019 = refactor_prof(survey_df_2019)" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Developer 59502\n", + "Student 8382\n", + "Non developer 6531\n", + "Novoice 2370\n", + "Ex-Developer 1210\n", + "Name: Profession, dtype: int64" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Profession'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## EdLevel" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Bachelor’s degree (BA, BS, B.Eng., etc.) 34926\n", + "Master’s degree (MA, MS, M.Eng., MBA, etc.) 17305\n", + "Some college/university study without earning a degree 9571\n", + "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.) 7638\n", + "Associate degree 2585\n", + "Other doctoral degree (Ph.D, Ed.D., etc.) 2032\n", + "Professional degree (JD, MD, etc.) 1037\n", + "Primary/elementary school 981\n", + "I never completed any formal education 352\n", + "Name: EdLevel, dtype: int64" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['EdLevel'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1568" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['EdLevel'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [], + "source": [ + "# Refactoring EdLevel\n", + "def refactor_ed(df):\n", + " '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n", + " conditions_ed = [(df['EdLevel'] == 'Bachelor’s degree (BA, BS, B.Eng., etc.)'),\n", + " (df['EdLevel'] == 'Master’s degree (MA, MS, M.Eng., MBA, etc.)'),\n", + " (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'), \n", + " (df['EdLevel'] == 'Associate degree'),\n", + " (df['EdLevel'] == 'Other doctoral degree (Ph.D, Ed.D., etc.)'),\n", + " (df['EdLevel'] == 'Some college/university study without earning a degree') \n", + " | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n", + " | (df['EdLevel'] == 'Primary/elementary school')\n", + " | (df['EdLevel'] == 'I never completed any formal education')]\n", + "\n", + " choices_ed = ['Bachelors', 'Masters', 'Professional', 'Associate', 'Doctorate', 'No Degree']\n", + "\n", + " df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n", + " \n", + " return df\n", + "\n", + "# applying function to subsets\n", + "survey_df_2019 = refactor_ed(survey_df_2019)\n", + "survey_df_2019['EdLevel'].replace('nan', 'Bachelors', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Bachelors 36494\n", + "No Degree 18542\n", + "Masters 17305\n", + "Associate 2585\n", + "Doctorate 2032\n", + "Professional 1037\n", + "Name: EdLevel, dtype: int64" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['EdLevel'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019.isnull().sum()['EdLevel']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Undergrad major" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Computer science, computer engineering, or software engineering 42211\n", + "Another engineering discipline (ex. civil, electrical, mechanical) 5472\n", + "Information systems, information technology, or system administration 4646\n", + "Web development or web design 2975\n", + "A natural science (ex. biology, chemistry, physics) 2866\n", + "Mathematics or statistics 2557\n", + "A business discipline (ex. accounting, finance, marketing) 1633\n", + "A humanities discipline (ex. literature, history, philosophy) 1408\n", + "A social science (ex. anthropology, psychology, political science) 1246\n", + "Fine arts or performing arts (ex. graphic design, music, studio art) 1124\n", + "Name: UndergradMajor, dtype: int64" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['UndergradMajor'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10787" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['UndergradMajor'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019['UndergradMajor'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Computer science, computer engineering, or software engineering 49010\n", + "Another engineering discipline (ex. civil, electrical, mechanical) 6368\n", + "Information systems, information technology, or system administration 5392\n", + "Web development or web design 3424\n", + "A natural science (ex. biology, chemistry, physics) 3285\n", + "Mathematics or statistics 2984\n", + "A business discipline (ex. accounting, finance, marketing) 1908\n", + "A humanities discipline (ex. literature, history, philosophy) 1627\n", + "A social science (ex. anthropology, psychology, political science) 1431\n", + "Fine arts or performing arts (ex. graphic design, music, studio art) 1327\n", + "I never declared a major 922\n", + "A health science (ex. nursing, pharmacy, radiology) 316\n", + "Name: UndergradMajor, dtype: int64" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['UndergradMajor'].value_counts().nlargest(15)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['UndergradMajor'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019.dropna(subset=['UndergradMajor'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 145, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['UndergradMajor'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Refactoring UndergradMajor\n", + "def refactor_major(df):\n", + " '''function to change undergrad major category to Computer Science, Engineering, Info Systems, Math/Stat, \n", + " Other Science, Web Design/Dev, Business, Arts and Science'''\n", + " \n", + " \n", + " conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'),\n", + " (df['UndergradMajor'] == 'Another engineering discipline (ex. civil, electrical, mechanical)'),\n", + " (df['UndergradMajor'] == 'Information systems, information technology, or system administration'),\n", + " (df['UndergradMajor'] == 'Mathematics or statistics'),\n", + " (df['UndergradMajor'] == 'I never declared a major'),\n", + " (df['UndergradMajor'] == 'A natural science (ex. biology, chemistry, physics)')\n", + " |(df['UndergradMajor'] == 'A health science (ex. nursing, pharmacy, radiology)'),\n", + " (df['UndergradMajor'] == 'Web development or web design'),\n", + " (df['UndergradMajor'] == 'A business discipline (ex. accounting, finance, marketing)'),\n", + " (df['UndergradMajor'] == 'A humanities discipline (ex. literature, history, philosophy)')\n", + " | (df['UndergradMajor'] == 'A social science (ex. anthropology, psychology, political science)')\n", + " | (df['UndergradMajor'] == 'Fine arts or performing arts (ex. graphic design, music, studio art)')]\n", + "\n", + " choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'No Major', 'Other Science',\n", + " 'Web Design/Dev', 'Business', 'Arts and Science']\n", + "\n", + " df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n", + " \n", + " return df\n", + "\n", + "# applying function to subsets\n", + "survey_df_2019 = refactor_major(survey_df_2019)" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Computer Science 49010\n", + "Engineering 6368\n", + "Info Systems 5392\n", + "Arts and Science 4385\n", + "Other Science 3601\n", + "Web Design/Dev 3424\n", + "Math/Stat 2984\n", + "Business 1908\n", + "No Major 922\n", + "Name: UndergradMajor, dtype: int64" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['UndergradMajor'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Job Status" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "I’m not actively looking, but I am open to new opportunities 42258\n", + "I am not interested in new job opportunities 19161\n", + "I am actively looking for a job 10491\n", + "Name: JobStatus, dtype: int64" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['JobStatus'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6084" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['JobStatus'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019['JobStatus'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['JobStatus'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "I’m not actively looking, but I am open to new opportunities 45917\n", + "I am not interested in new job opportunities 20712\n", + "I am actively looking for a job 11365\n", + "Name: JobStatus, dtype: int64" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['JobStatus'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019.dropna(subset=['JobStatus'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# refactoring JobStatus\n", + "# changing the jobstatus to seeking and non seeking\n", + "def refactor_job(df):\n", + " '''function to change JobStatus category to Seeking and Non Seeking'''\n", + " \n", + " conditions_job = [(df['JobStatus'] == 'I am actively looking for a job'),\n", + " (df['JobStatus'] == 'I am not interested in new job opportunities')\n", + " | (df['JobStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n", + " \n", + " choices_job = ['Seeking', 'Not seeking']\n", + " \n", + " df['JobStatus'] = np.select(conditions_job, choices_job, default=np.nan)\n", + " \n", + " return df\n", + "\n", + "survey_df_2019 = refactor_job(survey_df_2019)" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Not seeking 66629\n", + "Seeking 11365\n", + "Name: JobStatus, dtype: int64" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['JobStatus'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## JobSatisfaction" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Very satisfied 26584\n", + "Slightly satisfied 22739\n", + "Slightly dissatisfied 6843\n", + "Neither satisfied nor dissatisfied 6158\n", + "Very dissatisfied 3203\n", + "Name: JobSatisfaction, dtype: int64" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['JobSatisfaction'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12467" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['JobSatisfaction'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019['JobSatisfaction'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['JobSatisfaction'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Very satisfied 31507\n", + "Slightly satisfied 26970\n", + "Slightly dissatisfied 8343\n", + "Neither satisfied nor dissatisfied 7313\n", + "Very dissatisfied 3861\n", + "Name: JobSatisfaction, dtype: int64" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['JobSatisfaction'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Employment" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Employed full-time 58069\n", + "Independent contractor, freelancer, or self-employed 7305\n", + "Not employed, but looking for work 4703\n", + "Employed part-time 3958\n", + "Not employed, and not looking for work 2914\n", + "Retired 76\n", + "Name: Employment, dtype: int64" + ] + }, + "execution_count": 161, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Employment'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "969" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Employment'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019['Employment'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Employment'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Employed full-time 58771\n", + "Independent contractor, freelancer, or self-employed 7397\n", + "Not employed, but looking for work 4770\n", + "Employed part-time 4017\n", + "Not employed, and not looking for work 2960\n", + "Retired 79\n", + "Name: Employment, dtype: int64" + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Employment'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [], + "source": [ + "#Refactoring the employment\n", + "def refactor_emp(df):\n", + " '''function to change Employment category to Full-time, Self-employed, Not employed, Part-time '''\n", + " conditions_emp = [(df['Employment'] == 'Employed full-time'),\n", + " (df['Employment'] == 'Independent contractor, freelancer, or self-employed'),\n", + " (df['Employment'] == 'Not employed, but looking for work')\n", + " | (df['Employment'] == 'Not employed, and not looking for work')\n", + " | (df['Employment'] == 'Retired'),\n", + " (df['Employment'] == 'Employed part-time')]\n", + " \n", + " choices_emp = ['Full-time', 'Self-employed', 'Not employed', 'Part-time']\n", + " \n", + " df['Employment'] = np.select(conditions_emp, choices_emp, default=np.nan)\n", + " \n", + " return df\n", + "\n", + "\n", + "survey_df_2019 = refactor_emp(survey_df_2019)" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Full-time 58771\n", + "Not employed 7809\n", + "Self-employed 7397\n", + "Part-time 4017\n", + "Name: Employment, dtype: int64" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Employment'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ethnicity" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [], + "source": [ + "ethnicity_list = survey_df_2019['Ethnicity'].unique().tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[nan,\n", + " 'White or of European descent',\n", + " 'White or of European descent;Multiracial',\n", + " 'East Asian',\n", + " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial;Multiracial',\n", + " 'Black or of African descent',\n", + " 'Hispanic or Latino/Latina;Multiracial',\n", + " 'Hispanic or Latino/Latina',\n", + " 'Middle Eastern',\n", + " 'South Asian',\n", + " 'Multiracial',\n", + " 'East Asian;South Asian',\n", + " 'Biracial',\n", + " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n", + " 'Black or of African descent;White or of European descent;Biracial',\n", + " 'Middle Eastern;White or of European descent',\n", + " 'Native American, Pacific Islander, or Indigenous Australian',\n", + " 'Black or of African descent;White or of European descent',\n", + " 'White or of European descent;Biracial;Multiracial',\n", + " 'Hispanic or Latino/Latina;White or of European descent',\n", + " 'East Asian;White or of European descent;Biracial',\n", + " 'Black or of African descent;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n", + " 'East Asian;White or of European descent',\n", + " 'Hispanic or Latino/Latina;White or of European descent;Biracial',\n", + " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n", + " 'Hispanic or Latino/Latina;White or of European descent;Multiracial',\n", + " 'South Asian;White or of European descent;Multiracial',\n", + " 'South Asian;Biracial',\n", + " 'Middle Eastern;South Asian',\n", + " 'East Asian;South Asian;Multiracial',\n", + " 'White or of European descent;Biracial',\n", + " 'East Asian;Biracial;Multiracial',\n", + " 'Black or of African descent;Hispanic or Latino/Latina',\n", + " 'East Asian;Hispanic or Latino/Latina;White or of European descent',\n", + " 'East Asian;White or of European descent;Multiracial',\n", + " 'South Asian;White or of European descent;Biracial',\n", + " 'East Asian;South Asian;White or of European descent;Multiracial',\n", + " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n", + " 'South Asian;White or of European descent;Biracial;Multiracial',\n", + " 'Black or of African descent;White or of European descent;Multiracial',\n", + " 'Black or of African descent;Hispanic or Latino/Latina;White or of European descent',\n", + " 'Hispanic or Latino/Latina;Middle Eastern;White or of European descent',\n", + " 'Hispanic or Latino/Latina;Biracial',\n", + " 'Hispanic or Latino/Latina;South Asian;Multiracial',\n", + " 'Black or of African descent;East Asian;South Asian;White or of European descent;Biracial;Multiracial',\n", + " 'Black or of African descent;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n", + " 'East Asian;Middle Eastern',\n", + " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial',\n", + " 'Black or of African descent;Multiracial',\n", + " 'Middle Eastern;White or of European descent;Biracial',\n", + " 'East Asian;Middle Eastern;South Asian',\n", + " 'East Asian;Biracial',\n", + " 'Middle Eastern;White or of European descent;Multiracial',\n", + " 'Black or of African descent;Biracial',\n", + " 'Black or of African descent;Hispanic or Latino/Latina;White or of European descent;Multiracial',\n", + " 'Middle Eastern;Multiracial',\n", + " 'Black or of African descent;Middle Eastern',\n", + " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n", + " 'Black or of African descent;South Asian;Multiracial',\n", + " 'East Asian;Hispanic or Latino/Latina',\n", + " 'South Asian;Multiracial',\n", + " 'East Asian;South Asian;White or of European descent;Biracial;Multiracial',\n", + " 'Black or of African descent;Hispanic or Latino/Latina;Multiracial',\n", + " 'South Asian;White or of European descent',\n", + " 'Black or of African descent;East Asian',\n", + " 'Black or of African descent;Middle Eastern;Multiracial',\n", + " 'Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial',\n", + " 'Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n", + " 'Native American, Pacific Islander, or Indigenous Australian;South Asian',\n", + " 'Black or of African descent;South Asian;White or of European descent;Multiracial',\n", + " 'East Asian;White or of European descent;Biracial;Multiracial',\n", + " 'Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n", + " 'Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n", + " 'Middle Eastern;Biracial',\n", + " 'East Asian;Multiracial',\n", + " 'Black or of African descent;East Asian;Hispanic or Latino/Latina',\n", + " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial',\n", + " 'Hispanic or Latino/Latina;Biracial;Multiracial',\n", + " 'Black or of African descent;South Asian',\n", + " 'Black or of African descent;East Asian;White or of European descent;Multiracial',\n", + " 'Hispanic or Latino/Latina;White or of European descent;Biracial;Multiracial',\n", + " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n", + " 'Black or of African descent;White or of European descent;Biracial;Multiracial',\n", + " 'East Asian;Hispanic or Latino/Latina;South Asian',\n", + " 'East Asian;Middle Eastern;White or of European descent;Biracial',\n", + " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n", + " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian',\n", + " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian',\n", + " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Biracial',\n", + " 'East Asian;Native American, Pacific Islander, or Indigenous Australian',\n", + " 'Black or of African descent;Hispanic or Latino/Latina;Biracial',\n", + " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;South Asian',\n", + " 'Middle Eastern;South Asian;Multiracial',\n", + " 'Black or of African descent;Middle Eastern;White or of European descent',\n", + " 'Black or of African descent;Biracial;Multiracial',\n", + " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial;Multiracial',\n", + " 'Hispanic or Latino/Latina;Middle Eastern',\n", + " 'Black or of African descent;Middle Eastern;Biracial',\n", + " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian;Biracial',\n", + " 'Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial;Multiracial',\n", + " 'Black or of African descent;Hispanic or Latino/Latina;Middle Eastern;Multiracial',\n", + " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n", + " 'East Asian;Middle Eastern;South Asian;White or of European descent',\n", + " 'East Asian;Middle Eastern;White or of European descent;Multiracial',\n", + " 'Black or of African descent;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n", + " 'Biracial;Multiracial',\n", + " 'Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n", + " 'Hispanic or Latino/Latina;Middle Eastern;White or of European descent;Multiracial',\n", + " 'Hispanic or Latino/Latina;Middle Eastern;Biracial',\n", + " 'East Asian;Middle Eastern;White or of European descent',\n", + " 'East Asian;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n", + " 'Black or of African descent;East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n", + " 'East Asian;Hispanic or Latino/Latina;Middle Eastern;White or of European descent',\n", + " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n", + " 'Black or of African descent;South Asian;White or of European descent',\n", + " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;South Asian;Biracial',\n", + " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n", + " 'East Asian;South Asian;Biracial',\n", + " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n", + " 'East Asian;Hispanic or Latino/Latina;White or of European descent;Multiracial',\n", + " 'East Asian;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian',\n", + " 'Hispanic or Latino/Latina;South Asian;White or of European descent',\n", + " 'Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n", + " 'Black or of African descent;East Asian;Biracial',\n", + " 'East Asian;Hispanic or Latino/Latina;Biracial',\n", + " 'East Asian;South Asian;White or of European descent',\n", + " 'Black or of African descent;Hispanic or Latino/Latina;Middle Eastern;Biracial',\n", + " 'Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n", + " 'East Asian;Hispanic or Latino/Latina;Biracial;Multiracial',\n", + " 'Hispanic or Latino/Latina;South Asian',\n", + " 'East Asian;South Asian;Biracial;Multiracial',\n", + " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n", + " 'Native American, Pacific Islander, or Indigenous Australian;Biracial;Multiracial',\n", + " 'Black or of African descent;Hispanic or Latino/Latina;White or of European descent;Biracial;Multiracial',\n", + " 'Hispanic or Latino/Latina;South Asian;White or of European descent;Multiracial',\n", + " 'Hispanic or Latino/Latina;South Asian;Biracial',\n", + " 'East Asian;Middle Eastern;Multiracial',\n", + " 'Black or of African descent;East Asian;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Multiracial',\n", + " 'Black or of African descent;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n", + " 'Middle Eastern;South Asian;White or of European descent',\n", + " 'Middle Eastern;White or of European descent;Biracial;Multiracial',\n", + " 'Black or of African descent;East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n", + " 'East Asian;Hispanic or Latino/Latina;Middle Eastern',\n", + " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;White or of European descent;Multiracial',\n", + " 'Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n", + " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n", + " 'East Asian;Middle Eastern;South Asian;Multiracial',\n", + " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;Biracial;Multiracial',\n", + " 'Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;South Asian;White or of European descent;Biracial;Multiracial',\n", + " 'East Asian;Hispanic or Latino/Latina;Multiracial',\n", + " 'Black or of African descent;East Asian;Middle Eastern;White or of European descent;Biracial;Multiracial',\n", + " 'Hispanic or Latino/Latina;Middle Eastern;White or of European descent;Biracial',\n", + " 'Black or of African descent;Middle Eastern;South Asian',\n", + " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;Biracial;Multiracial',\n", + " 'East Asian;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;White or of European descent',\n", + " 'South Asian;Biracial;Multiracial',\n", + " 'Black or of African descent;Hispanic or Latino/Latina;South Asian',\n", + " 'Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Multiracial',\n", + " 'East Asian;Hispanic or Latino/Latina;Middle Eastern;South Asian',\n", + " 'Hispanic or Latino/Latina;Middle Eastern;Multiracial',\n", + " 'Black or of African descent;East Asian;Multiracial',\n", + " 'Black or of African descent;Native American, Pacific Islander, or Indigenous Australian;Multiracial',\n", + " 'East Asian;Middle Eastern;South Asian;White or of European descent;Multiracial',\n", + " 'Black or of African descent;East Asian;South Asian',\n", + " 'Black or of African descent;Hispanic or Latino/Latina;Biracial;Multiracial',\n", + " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;Biracial',\n", + " 'East Asian;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Biracial;Multiracial',\n", + " 'Black or of African descent;East Asian;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;White or of European descent;Multiracial',\n", + " 'Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent',\n", + " 'Black or of African descent;South Asian;Biracial',\n", + " 'East Asian;Hispanic or Latino/Latina;Native American, Pacific Islander, or Indigenous Australian;South Asian',\n", + " 'East Asian;South Asian;White or of European descent;Biracial',\n", + " 'Black or of African descent;East Asian;Middle Eastern;South Asian;Multiracial']" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#here, you can see that we have long list of values. lets refactor them\n", + "ethnicity_list" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "173" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(ethnicity_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [], + "source": [ + "#refactoring long list of values into categories.\n", + "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Biracial', na=False), 'Ethnicity'] = 'Biracial'\n", + "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Black or of African descent', na=False), 'Ethnicity'] = 'Black or of African descent'\n", + "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('East Asian', na=False), 'Ethnicity'] = 'East Asian'\n", + "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Hispanic or Latino', na=False), 'Ethnicity'] = 'Hispanic or Latino'\n", + "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Indigenous', na=False), 'Ethnicity'] = 'Indigenous'\n", + "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Middle Eastern', na=False), 'Ethnicity'] = 'Middle Eastern'\n", + "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('South Asian', na=False), 'Ethnicity'] = 'South Asian'\n", + "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('White or of European descent', na=False), 'Ethnicity'] = 'White or of European descent'\n", + "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Multiracial', na=False), 'Ethnicity'] = 'Multiracial'\n", + "survey_df_2019.loc[survey_df_2019['Ethnicity'].str.match('Native American', na=False), 'Ethnicity'] = 'Native American'" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7804" + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Ethnicity'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "White or of European descent 47587\n", + "South Asian 7417\n", + "Hispanic or Latino 4901\n", + "East Asian 3698\n", + "Middle Eastern 3057\n", + "Black or of African descent 2360\n", + "Multiracial 572\n", + "Native American 322\n", + "Biracial 276\n", + "Name: Ethnicity, dtype: int64" + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Ethnicity'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019['Ethnicity']=survey_df_2019.groupby(['Country'])['Ethnicity'].bfill().ffill()" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Ethnicity'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "White or of European descent 50883\n", + "South Asian 10061\n", + "Hispanic or Latino 5204\n", + "East Asian 4391\n", + "Middle Eastern 3596\n", + "Black or of African descent 2570\n", + "Multiracial 632\n", + "Native American 355\n", + "Biracial 302\n", + "Name: Ethnicity, dtype: int64" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Ethnicity'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dependents" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "No 46457\n", + "Yes 28918\n", + "Name: Dependents, dtype: int64" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019[\"Dependents\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2619" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019[\"Dependents\"].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [], + "source": [ + "#Lets consider that people who didnt respond has no dependents for the purpose of analysis\n", + "survey_df_2019[\"Dependents\"].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 180, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019[\"Dependents\"].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "No 48085\n", + "Yes 29909\n", + "Name: Dependents, dtype: int64" + ] + }, + "execution_count": 181, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019[\"Dependents\"].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DevType" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5025" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['DevType'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Developer, full-stack 7636\n", + "Developer, back-end 4387\n", + "Developer, back-end;Developer, front-end;Developer, full-stack 2216\n", + "Developer, front-end 1985\n", + "Developer, mobile 1934\n", + "Developer, back-end;Developer, full-stack 1886\n", + "Student 1289\n", + "Developer, front-end;Developer, full-stack 940\n", + "Developer, desktop or enterprise applications 900\n", + "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack 815\n", + "Name: DevType, dtype: int64" + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['DevType'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019['DevType'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 185, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['DevType'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Developer, full-stack 8147\n", + "Developer, back-end 4680\n", + "Developer, back-end;Developer, front-end;Developer, full-stack 2365\n", + "Developer, front-end 2129\n", + "Developer, mobile 2086\n", + "Name: DevType, dtype: int64" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['DevType'].value_counts().nlargest()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LanguageWorkedWith" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "656" + ] + }, + "execution_count": 187, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['LanguageWorkedWith'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "HTML/CSS;JavaScript;PHP;SQL 1345\n", + "C#;HTML/CSS;JavaScript;SQL 1282\n", + "HTML/CSS;JavaScript 1098\n", + "C#;HTML/CSS;JavaScript;SQL;TypeScript 908\n", + "HTML/CSS;JavaScript;PHP 821\n", + "Java 757\n", + "HTML/CSS;JavaScript;TypeScript 644\n", + "Python 634\n", + "HTML/CSS;Java;JavaScript;SQL 596\n", + "C# 484\n", + "Name: LanguageWorkedWith, dtype: int64" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['LanguageWorkedWith'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019['LanguageWorkedWith'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['LanguageWorkedWith'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "HTML/CSS;JavaScript;PHP;SQL 1366\n", + "C#;HTML/CSS;JavaScript;SQL 1288\n", + "HTML/CSS;JavaScript 1108\n", + "C#;HTML/CSS;JavaScript;SQL;TypeScript 914\n", + "HTML/CSS;JavaScript;PHP 831\n", + "Java 765\n", + "HTML/CSS;JavaScript;TypeScript 650\n", + "Python 640\n", + "HTML/CSS;Java;JavaScript;SQL 600\n", + "C# 489\n", + "Name: LanguageWorkedWith, dtype: int64" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['LanguageWorkedWith'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CompetenceLevel" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "A little above average 29693\n", + "Average 15532\n", + "Far above average 13840\n", + "A little below average 4837\n", + "Far below average 1322\n", + "Name: CompetenceLevel, dtype: int64" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['CompetenceLevel'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12770" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['CompetenceLevel'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [], + "source": [ + "#Assign the null values based on forward fill.\n", + "survey_df_2019['CompetenceLevel'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['CompetenceLevel'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "A little above average 35394\n", + "Average 18436\n", + "Far above average 16821\n", + "A little below average 5739\n", + "Far below average 1604\n", + "Name: CompetenceLevel, dtype: int64" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['CompetenceLevel'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CurrentJobSatis" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Slightly satisfied 22123\n", + "Very satisfied 20452\n", + "Slightly dissatisfied 9751\n", + "Neither satisfied nor dissatisfied 7547\n", + "Very dissatisfied 4283\n", + "Name: CurrentJobSatis, dtype: int64" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['CurrentJobSatis'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13838" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['CurrentJobSatis'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [], + "source": [ + "#Assign the null values based on forward fill.\n", + "survey_df_2019['CurrentJobSatis'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 200, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['CurrentJobSatis'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Slightly satisfied 26780\n", + "Very satisfied 24873\n", + "Slightly dissatisfied 12043\n", + "Neither satisfied nor dissatisfied 9111\n", + "Very dissatisfied 5187\n", + "Name: CurrentJobSatis, dtype: int64" + ] + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['CurrentJobSatis'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LanguageDesireNextYear" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 1003\n", + "HTML/CSS;JavaScript 624\n", + "HTML/CSS;JavaScript;TypeScript 569\n", + "C# 533\n", + "C#;HTML/CSS;JavaScript;SQL 525\n", + "C#;HTML/CSS;JavaScript;SQL;TypeScript 515\n", + "HTML/CSS;JavaScript;PHP;SQL 472\n", + "Java 457\n", + "Go 373\n", + "HTML/CSS;JavaScript;Python 354\n", + "Swift 348\n", + "Kotlin 335\n", + "HTML/CSS;JavaScript;PHP 326\n", + "C++;Python 324\n", + "C#;SQL 309\n", + "JavaScript 307\n", + "C++ 306\n", + "C#;HTML/CSS;JavaScript;TypeScript 297\n", + "Java;Kotlin 280\n", + "JavaScript;Python 275\n", + "Name: LanguageDesireNextYear, dtype: int64" + ] + }, + "execution_count": 202, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['LanguageDesireNextYear'].value_counts().nlargest(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3424" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['LanguageDesireNextYear'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [], + "source": [ + "#Assign the null values based on forward fill.\n", + "survey_df_2019['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 205, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['LanguageDesireNextYear'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 1054\n", + "HTML/CSS;JavaScript 656\n", + "HTML/CSS;JavaScript;TypeScript 597\n", + "C# 557\n", + "C#;HTML/CSS;JavaScript;SQL 553\n", + "C#;HTML/CSS;JavaScript;SQL;TypeScript 533\n", + "HTML/CSS;JavaScript;PHP;SQL 493\n", + "Java 484\n", + "Go 397\n", + "HTML/CSS;JavaScript;Python 370\n", + "Swift 360\n", + "Kotlin 360\n", + "HTML/CSS;JavaScript;PHP 347\n", + "C++;Python 336\n", + "C#;SQL 320\n", + "C++ 319\n", + "JavaScript 312\n", + "C#;HTML/CSS;JavaScript;TypeScript 305\n", + "Java;Kotlin 298\n", + "JavaScript;Python 289\n", + "Name: LanguageDesireNextYear, dtype: int64" + ] + }, + "execution_count": 206, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['LanguageDesireNextYear'].value_counts().nlargest(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## YearsCodePro" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 207, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['YearsCodePro'].value_counts" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [], + "source": [ + "#changing the dtype to float\n", + "survey_df_2019['YearsCodePro'] = survey_df_2019['YearsCodePro'].apply(pd.to_numeric, errors='coerce')" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.0 7243\n", + "3.0 7164\n", + "5.0 5855\n", + "4.0 5764\n", + "6.0 4133\n", + "1.0 3995\n", + "10.0 3934\n", + "7.0 3374\n", + "8.0 3166\n", + "12.0 2008\n", + "Name: YearsCodePro, dtype: int64" + ] + }, + "execution_count": 209, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['YearsCodePro'].value_counts().head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14639" + ] + }, + "execution_count": 210, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['YearsCodePro'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019['YearsCodePro'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 212, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['YearsCodePro'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019.dropna(subset=['YearsCodePro'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 214, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['YearsCodePro'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.0 8853\n", + "3.0 8843\n", + "5.0 7186\n", + "4.0 7124\n", + "6.0 5103\n", + "1.0 4925\n", + "10.0 4830\n", + "7.0 4146\n", + "8.0 3910\n", + "12.0 2487\n", + "Name: YearsCodePro, dtype: int64" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['YearsCodePro'].value_counts().head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Country" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "United States 18335\n", + "India 7276\n", + "Germany 5316\n", + "United Kingdom 5130\n", + "Canada 2976\n", + "France 2225\n", + "Brazil 1860\n", + "Poland 1773\n", + "Netherlands 1687\n", + "Australia 1657\n", + "Russian Federation 1551\n", + "Spain 1477\n", + "Italy 1451\n", + "Sweden 1165\n", + "Switzerland 884\n", + "Name: Country, dtype: int64" + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Country'].value_counts().nlargest(15)" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "survey_df_2019['Country'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019['Country'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 219, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Country'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "United States 18335\n", + "India 7276\n", + "Germany 5316\n", + "United Kingdom 5130\n", + "Canada 2976\n", + "France 2225\n", + "Brazil 1860\n", + "Poland 1773\n", + "Netherlands 1687\n", + "Australia 1657\n", + "Russian Federation 1551\n", + "Spain 1477\n", + "Italy 1451\n", + "Sweden 1165\n", + "Switzerland 884\n", + "Name: Country, dtype: int64" + ] + }, + "execution_count": 220, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['Country'].value_counts().nlargest(15)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## SalaryUSD" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2000000.0 667\n", + "1000000.0 529\n", + "120000.0 475\n", + "100000.0 450\n", + "150000.0 399\n", + "Name: SalaryUSD, dtype: int64" + ] + }, + "execution_count": 221, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['SalaryUSD'].value_counts().nlargest()" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "24805" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['SalaryUSD'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019['SalaryUSD'] = survey_df_2019.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform(lambda grp: grp.fillna(np.mean(grp)))" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3537" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "survey_df_2019['SalaryUSD'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2000000.0 669\n", + "1000000.0 547\n", + "150000.0 494\n", + "120000.0 476\n", + "100000.0 450\n", + "Name: SalaryUSD, dtype: int64" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "survey_df_2019['SalaryUSD'].value_counts().nlargest()" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [], + "source": [ + "country_mean_salary = survey_df_2019.groupby('Country')['SalaryUSD'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Country\n", + "Liechtenstein 811188.000000\n", + "San Marino 301788.000000\n", + "Ireland 247051.427005\n", + "Swaziland 242607.500000\n", + "United States 240269.159270\n", + "Timor-Leste 229500.000000\n", + "Qatar 203892.571429\n", + "Republic of Korea 174593.739130\n", + "Norway 173173.193026\n", + "Andorra 171862.000000\n", + "Name: SalaryUSD, dtype: float64" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "country_mean_salary.nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [], + "source": [ + "survey_df_2019.dropna(subset=['SalaryUSD'], inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleaned Data" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Age 0\n", + "JobSatisfaction 0\n", + "SalaryUSD 0\n", + "Country 0\n", + "Dependents 0\n", + "EdLevel 0\n", + "Employment 0\n", + "Ethnicity 0\n", + "Gender 0\n", + "Hobbyist 0\n", + "CompetenceLevel 0\n", + "CurrentJobSatis 0\n", + "JobStatus 0\n", + "LanguageDesireNextYear 0\n", + "LanguageWorkedWith 0\n", + "Profession 0\n", + "UndergradMajor 0\n", + "YearsCodePro 0\n", + "DevType 0\n", + "dtype: int64" + ] + }, + "execution_count": 229, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#handle all the null value\n", + "survey_df_2019.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [], + "source": [ + "#resetting the index values\n", + "survey_df_2019 = survey_df_2019.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of rows before cleaning the data is 88883\n", + "Number of rows after cleaning the data is 74457\n" + ] + } + ], + "source": [ + "cleaned_df_2019 = survey_df_2019[survey_df_2019.notnull()]\n", + "\n", + "print(f\"Number of rows before cleaning the data is {survey_main_df.shape[0]}\")\n", + "print(f\"Number of rows after cleaning the data is {cleaned_df_2019.shape[0]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [], + "source": [ + "cleaned_df_2019['Age']=cleaned_df_2019['Age'].astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeJobSatisfactionSalaryUSDCountryDependentsEdLevelEmploymentEthnicityGenderHobbyistCompetenceLevelCurrentJobSatisJobStatusLanguageDesireNextYearLanguageWorkedWithProfessionUndergradMajorYearsCodeProDevType
028Slightly satisfied8820.0ThailandYesBachelorsFull-timeEast AsianManYesAverageSlightly satisfiedNot seekingElixir;HTML/CSSHTML/CSSNon developerWeb Design/Dev1.0Designer;Developer, back-end;Developer, front-...
122Very satisfied61000.0United StatesNoBachelorsFull-timeWhite or of European descentManNoA little below averageSlightly satisfiedNot seekingC;C#;JavaScript;SQLC;C++;C#;Python;SQLDeveloperComputer Science1.0Developer, full-stack
230Very dissatisfied33184.8UkraineNoBachelorsFull-timeWhite or of European descentManYesA little above averageSlightly dissatisfiedNot seekingHTML/CSS;Java;JavaScript;SQL;WebAssemblyC++;HTML/CSS;Java;JavaScript;Python;SQL;VBADeveloperComputer Science9.0Academic researcher;Developer, desktop or ente...
328Very satisfied366420.0CanadaNoBachelorsFull-timeEast AsianManYesA little above averageSlightly satisfiedNot seekingPython;Scala;SQLJava;R;SQLNon developerMath/Stat3.0Data or business analyst;Data scientist or mac...
442Slightly satisfied36000.0UkraineYesBachelorsSelf-employedWhite or of European descentManNoAverageNeither satisfied nor dissatisfiedNot seekingHTML/CSS;JavaScriptHTML/CSS;JavaScriptDeveloperEngineering4.0Designer;Developer, front-end
\n", + "
" + ], + "text/plain": [ + " Age JobSatisfaction SalaryUSD Country Dependents EdLevel \\\n", + "0 28 Slightly satisfied 8820.0 Thailand Yes Bachelors \n", + "1 22 Very satisfied 61000.0 United States No Bachelors \n", + "2 30 Very dissatisfied 33184.8 Ukraine No Bachelors \n", + "3 28 Very satisfied 366420.0 Canada No Bachelors \n", + "4 42 Slightly satisfied 36000.0 Ukraine Yes Bachelors \n", + "\n", + " Employment Ethnicity Gender Hobbyist \\\n", + "0 Full-time East Asian Man Yes \n", + "1 Full-time White or of European descent Man No \n", + "2 Full-time White or of European descent Man Yes \n", + "3 Full-time East Asian Man Yes \n", + "4 Self-employed White or of European descent Man No \n", + "\n", + " CompetenceLevel CurrentJobSatis JobStatus \\\n", + "0 Average Slightly satisfied Not seeking \n", + "1 A little below average Slightly satisfied Not seeking \n", + "2 A little above average Slightly dissatisfied Not seeking \n", + "3 A little above average Slightly satisfied Not seeking \n", + "4 Average Neither satisfied nor dissatisfied Not seeking \n", + "\n", + " LanguageDesireNextYear \\\n", + "0 Elixir;HTML/CSS \n", + "1 C;C#;JavaScript;SQL \n", + "2 HTML/CSS;Java;JavaScript;SQL;WebAssembly \n", + "3 Python;Scala;SQL \n", + "4 HTML/CSS;JavaScript \n", + "\n", + " LanguageWorkedWith Profession \\\n", + "0 HTML/CSS Non developer \n", + "1 C;C++;C#;Python;SQL Developer \n", + "2 C++;HTML/CSS;Java;JavaScript;Python;SQL;VBA Developer \n", + "3 Java;R;SQL Non developer \n", + "4 HTML/CSS;JavaScript Developer \n", + "\n", + " UndergradMajor YearsCodePro \\\n", + "0 Web Design/Dev 1.0 \n", + "1 Computer Science 1.0 \n", + "2 Computer Science 9.0 \n", + "3 Math/Stat 3.0 \n", + "4 Engineering 4.0 \n", + "\n", + " DevType \n", + "0 Designer;Developer, back-end;Developer, front-... \n", + "1 Developer, full-stack \n", + "2 Academic researcher;Developer, desktop or ente... \n", + "3 Data or business analyst;Data scientist or mac... \n", + "4 Designer;Developer, front-end " + ] + }, + "execution_count": 233, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "cleaned_df_2019.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stackoverflow_Survey_Analysis 2020" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(r'C:\\Users\\sanja\\Stack_Data\\survey_results_public_2020.csv')\n", + "#df2020.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [], + "source": [ + "#drop unnecessary columns (drop Sabiha's part)\n", + "drop_cols = [ 'Age1stCode', 'CompFreq', 'CompTotal', 'CurrencyDesc', 'CurrencySymbol', 'NEWJobHunt','NEWJobHuntResearch', 'NEWLearn', \n", + " 'NEWOffTopic', 'NEWOnboardGood', 'NEWOtherComms', 'NEWOvertime', 'NEWPurchaseResearch', \n", + " 'NEWPurpleLink', 'NEWSOSites', 'NEWStuck', 'OpSys', 'OrgSize', 'PlatformDesireNextYear', 'PlatformWorkedWith',\n", + " 'PurchaseWhat', 'Respondent', 'SOAccount', 'SOComm', 'SOPartFreq', 'SOVisitFreq', 'Sexuality', 'SurveyEase', \n", + " 'SurveyLength', 'Trans', 'WebframeDesireNextYear', 'WebframeWorkedWith', 'WelcomeChange', 'WorkWeekHrs', 'YearsCode']\n", + "df.drop(drop_cols, axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [], + "source": [ + "#Selecting only the required columns for analysis\n", + "cols =['Age','Gender', 'ConvertedComp', 'Country', 'DevType', 'Hobbyist', 'EdLevel', 'Employment', \n", + " 'Ethnicity', 'JobSat', 'JobSeek', 'LanguageDesireNextYear', 'LanguageWorkedWith', 'MainBranch',\n", + " 'UndergradMajor', 'YearsCodePro']\n", + "df2020 = df[cols]\n", + "#df2020.head()\n", + "#df2020.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [], + "source": [ + "#changing the name of columns for easier understanding\n", + "# 'MainBranch': 'Profession'\n", + "# 'ConvertedComp': 'SalaryUSD'\n", + "# 'JobSat' : 'CurrentJobSatis'\n", + "# 'JobSeek' : 'JobStatus'\n", + "\n", + "df2020.rename(columns={'MainBranch': 'Profession', 'ConvertedComp': 'SalaryUSD', \n", + " 'JobSat' : 'CurrentJobSatis', 'JobSeek' : 'JobStatus' }, \n", + " inplace =True)" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Age 19015\n", + "Gender 13904\n", + "SalaryUSD 29705\n", + "Country 389\n", + "DevType 15091\n", + "Hobbyist 45\n", + "EdLevel 7030\n", + "Employment 607\n", + "Ethnicity 18513\n", + "CurrentJobSatis 19267\n", + "JobStatus 12734\n", + "LanguageDesireNextYear 10348\n", + "LanguageWorkedWith 7083\n", + "Profession 299\n", + "UndergradMajor 13466\n", + "YearsCodePro 18112\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "print(df2020.isnull().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total cell: 1031376\n", + "Total missing values: 371516\n", + "Missing: 36.02139278013062 %\n" + ] + } + ], + "source": [ + "#Finding % of missing data\n", + "missing_count = df.isnull().sum() #number of missing\n", + "total_cells = np.product(df2020.shape) # number of cells (cols x rows)\n", + "total_missing = missing_count.sum()\n", + "missing_percent = (total_missing*100)/total_cells\n", + "\n", + "print('Total cell: ', total_cells)\n", + "print('Total missing values: ', total_missing)\n", + "print('Missing: ', missing_percent, '%')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gender" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13904" + ] + }, + "execution_count": 240, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['Gender'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Gender\n", + "Man 46013\n", + "Man;Non-binary, genderqueer, or gender non-conforming 121\n", + "Non-binary, genderqueer, or gender non-conforming 385\n", + "Woman 3844\n", + "Woman;Man 76\n", + "Woman;Man;Non-binary, genderqueer, or gender non-conforming 26\n", + "Woman;Non-binary, genderqueer, or gender non-conforming 92\n", + "Name: Gender, dtype: int64" + ] + }, + "execution_count": 241, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Counting number of each gender\n", + "df2020.groupby('Gender')['Gender'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": {}, + "outputs": [], + "source": [ + "#Assigining the surveyors who havent mentioned their gender to Non-Binary category\n", + "df2020['Gender'] = df['Gender'].fillna('Non-binary') \n", + "\n", + "#Grouping genders into 3 groups Man, Womanand Non-binary\n", + "df2020['Gender'].replace('Man;Non-binary, genderqueer, or gender non-conforming', 'Man', inplace =True)\n", + "df2020['Gender'].replace('Woman;Non-binary, genderqueer, or gender non-conforming', 'Woman', inplace =True)\n", + "df2020['Gender'].replace('Woman;Man;Non-binary, genderqueer, or gender non-conforming', 'Non-binary', inplace =True)\n", + "df2020['Gender'].replace('Woman;Man', 'Non-binary', inplace =True)\n", + "df2020['Gender'].replace('Non-binary, genderqueer, or gender non-conforming', 'Non-binary', inplace =True)" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Gender\n", + "Man 46134\n", + "Non-binary 14391\n", + "Woman 3936\n", + "Name: Gender, dtype: int64" + ] + }, + "execution_count": 243, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Counting number of each gender after\n", + "df2020.groupby('Gender')['Gender'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "df shape after clean Gender: (64461, 16)\n" + ] + } + ], + "source": [ + "\n", + "print('df shape after clean Gender: ', df2020.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Age" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "19015" + ] + }, + "execution_count": 245, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['Age'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Plottig boxplot to check outliers\n", + "sns.boxplot(x='Age', y= 'Gender', data=df2020)\n", + "plt.title(\"Before cleaning Age's outliers from genders\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": {}, + "outputs": [], + "source": [ + "#Cleaning Age's outliers from each gender)\n", + "df2020 = df2020[(df['Age'] >= 15) & (df2020['Age'] <= 60)]" + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Plottig boxplot to check outliers after cleaning some outliers\n", + "sns.boxplot(x='Age', y= 'Gender', data=df2020)\n", + "plt.title(\"After cleaning Age's outliers from genders\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": {}, + "outputs": [], + "source": [ + "#fill Age's null values with mean of each gender\n", + "means = df2020.groupby('Gender')['Age'].transform('mean')\n", + "df2020['Age'] = df2020['Age'].fillna(means)\n", + "\n", + "#convert from float to int\n", + "df2020['Age'] = df2020['Age'].apply(str).str[:2]\n", + "df2020['Age'] = df2020['Age'].apply(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "df shape after clean Age: (44709, 16)\n" + ] + } + ], + "source": [ + "#df before 64461\n", + "print('df shape after clean Age: ', df2020.shape) #no. of Ages' outliners = 64461-44709=19752 (30.6%)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## EdLevel" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "933" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['EdLevel'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Bachelor’s degree (B.A., B.S., B.Eng., etc.) 20290\n", + "Master’s degree (M.A., M.S., M.Eng., MBA, etc.) 10000\n", + "Some college/university study without earning a degree 5699\n", + "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.) 3676\n", + "Associate degree (A.A., A.S., etc.) 1455\n", + "Other doctoral degree (Ph.D., Ed.D., etc.) 1256\n", + "Primary/elementary school 590\n", + "Professional degree (JD, MD, etc.) 578\n", + "I never completed any formal education 232\n", + "Name: EdLevel, dtype: int64" + ] + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['EdLevel'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": {}, + "outputs": [], + "source": [ + "#Refactoring EdLevel\n", + "def refactor_ed(df):\n", + " '''function to change Education level category to Bachelors, Masters, Professional, Associate, Doctorate, No Degree'''\n", + " conditions_ed = [(df['EdLevel'] == 'Associate degree (A.A., A.S., etc.)'),\n", + " (df['EdLevel'] == 'Bachelor’s degree (B.A., B.S., B.Eng., etc.)'),\n", + " (df['EdLevel'] == 'Master’s degree (M.A., M.S., M.Eng., MBA, etc.)'),\n", + " (df['EdLevel'] == 'Professional degree (JD, MD, etc.)'), \n", + " (df['EdLevel'] == 'Other doctoral degree (Ph.D., Ed.D., etc.)'),\n", + " (df['EdLevel'] == 'Some college/university study without earning a degree') \n", + " | (df['EdLevel'] == 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)') \n", + " | (df['EdLevel'] == 'Primary/elementary school')\n", + " | (df['EdLevel'] == 'I never completed any formal education')]\n", + " \n", + " choices_ed = ['Associate', 'Bachelors', 'Masters', 'Professional', 'Doctorate', 'No Degree']\n", + " df['EdLevel'] = np.select(conditions_ed, choices_ed, default = np.NaN)\n", + " return df\n", + "\n", + "# applying function to subsets\n", + "df2020 = refactor_ed(df2020)\n", + "#Assigining the surveyors who havent mentioned their education level to Bachelor’s degree\n", + "df2020['EdLevel'].replace('nan', 'Bachelors', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Bachelors 21223\n", + "No Degree 10197\n", + "Masters 10000\n", + "Associate 1455\n", + "Doctorate 1256\n", + "Professional 578\n", + "Name: EdLevel, dtype: int64" + ] + }, + "execution_count": 254, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['EdLevel'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## JobSat (CurrentJobSatis)" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8690" + ] + }, + "execution_count": 255, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['CurrentJobSatis'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Very satisfied 11751\n", + "Slightly satisfied 11198\n", + "Slightly dissatisfied 5790\n", + "Neither satisfied nor dissatisfied 4373\n", + "Very dissatisfied 2907\n", + "Name: CurrentJobSatis, dtype: int64" + ] + }, + "execution_count": 256, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['CurrentJobSatis'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": {}, + "outputs": [], + "source": [ + "df2020['CurrentJobSatis'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Very satisfied 14628\n", + "Slightly satisfied 13834\n", + "Slightly dissatisfied 7192\n", + "Neither satisfied nor dissatisfied 5446\n", + "Very dissatisfied 3609\n", + "Name: CurrentJobSatis, dtype: int64" + ] + }, + "execution_count": 258, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['CurrentJobSatis'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## JobSeek (JobStatus)" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2153" + ] + }, + "execution_count": 259, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['JobStatus'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 260, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "JobStatus\n", + "I am actively looking for a job 6980\n", + "I am not interested in new job opportunities 10919\n", + "I’m not actively looking, but I am open to new opportunities 24657\n", + "Name: JobStatus, dtype: int64" + ] + }, + "execution_count": 260, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020.groupby('JobStatus')['JobStatus'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": {}, + "outputs": [], + "source": [ + "df2020['JobStatus'].fillna(method='ffill', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": {}, + "outputs": [], + "source": [ + "#Refactoring JobStatus\n", + "#Changing the jobstatus to seeking and non seeking\n", + "def refactor_job(df):\n", + " '''function to change JobStatus category to Seeking and Non Seeking'''\n", + " \n", + " conditions_job = [(df['JobStatus'] == 'I am actively looking for a job'),\n", + " (df['JobStatus'] == 'I am not interested in new job opportunities')\n", + " | (df['JobStatus'] == 'I’m not actively looking, but I am open to new opportunities')]\n", + " \n", + " choices_job = ['Seeking', 'Not seeking']\n", + " df['JobSeek'] = np.select(conditions_job, choices_job, default=np.nan) \n", + " return df\n", + "\n", + "df2020 = refactor_job(df2020)" + ] + }, + { + "cell_type": "code", + "execution_count": 263, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "JobSeek\n", + "Not seeking 37369\n", + "Seeking 7340\n", + "Name: JobSeek, dtype: int64" + ] + }, + "execution_count": 263, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020.groupby('JobSeek')['JobSeek'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 264, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['JobStatus'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DevType" + ] + }, + { + "cell_type": "code", + "execution_count": 265, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5954" + ] + }, + "execution_count": 265, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['DevType'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Developer, full-stack 3399\n", + "Developer, back-end 2374\n", + "Developer, back-end;Developer, front-end;Developer, full-stack 1838\n", + "Developer, back-end;Developer, full-stack 1216\n", + "Developer, front-end 1071\n", + "Developer, mobile 953\n", + "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack 668\n", + "Developer, front-end;Developer, full-stack 667\n", + "Developer, back-end;Developer, desktop or enterprise applications 528\n", + "Developer, back-end;Developer, front-end;Developer, full-stack;Developer, mobile 475\n", + "Name: DevType, dtype: int64" + ] + }, + "execution_count": 266, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['DevType'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": {}, + "outputs": [], + "source": [ + "df2020['DevType'] = df2020['DevType'].bfill().ffill()" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Developer, full-stack 3940\n", + "Developer, back-end 2721\n", + "Developer, back-end;Developer, front-end;Developer, full-stack 2146\n", + "Developer, back-end;Developer, full-stack 1411\n", + "Developer, front-end 1229\n", + "Developer, mobile 1074\n", + "Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack 779\n", + "Developer, front-end;Developer, full-stack 758\n", + "Developer, back-end;Developer, desktop or enterprise applications 617\n", + "Developer, back-end;Developer, front-end;Developer, full-stack;Developer, mobile 532\n", + "Name: DevType, dtype: int64" + ] + }, + "execution_count": 268, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['DevType'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(64461, 26)" + ] + }, + "execution_count": 269, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 270, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "df2020['DevType'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
AgeGenderSalaryUSDCountryDevTypeHobbyistEdLevelEmploymentEthnicityCurrentJobSatisJobStatusLanguageDesireNextYearLanguageWorkedWithProfessionUndergradMajorYearsCodeProJobSeek
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [Age, Gender, SalaryUSD, Country, DevType, Hobbyist, EdLevel, Employment, Ethnicity, CurrentJobSatis, JobStatus, LanguageDesireNextYear, LanguageWorkedWith, Profession, UndergradMajor, YearsCodePro, JobSeek]\n", + "Index: []" + ] + }, + "execution_count": 271, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020[df2020['DevType'].isnull()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ethnicity" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4051" + ] + }, + "execution_count": 272, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "df2020['Ethnicity'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "White or of European descent 26552\n", + "South Asian 3707\n", + "Hispanic or Latino/a/x 2078\n", + "Middle Eastern 1417\n", + "Southeast Asian 1371\n", + "East Asian 1342\n", + "Black or of African descent 1327\n", + "Hispanic or Latino/a/x;White or of European descent 720\n", + "Middle Eastern;White or of European descent 344\n", + "Multiracial 245\n", + "Name: Ethnicity, dtype: int64" + ] + }, + "execution_count": 273, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#count number of each Ethnicity\n", + "df2020.groupby('Ethnicity')['Ethnicity'].count()\n", + "df2020['Ethnicity'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#combine Ethnicity by str.match(if each string starts with a match of a regular expression pattern)\n", + "df2020.loc[df['Ethnicity'].str.match('Biracial') == True, 'Ethnicity'] = 'Biracial'\n", + "df2020.loc[df['Ethnicity'].str.match('Black or of African descent') == True, 'Ethnicity'] = 'Black or of African descent'\n", + "df2020.loc[df['Ethnicity'].str.match('East Asian') == True, 'Ethnicity'] = 'East Asian'\n", + "df2020.loc[df['Ethnicity'].str.match('Hispanic or Latino') == True, 'Ethnicity'] = 'Hispanic or Latino'\n", + "df2020.loc[df['Ethnicity'].str.match('Indigenous') == True, 'Ethnicity'] = 'Indigenous'\n", + "df2020.loc[df['Ethnicity'].str.match('Middle Eastern') == True, 'Ethnicity'] = 'Middle Eastern'\n", + "df2020.loc[df['Ethnicity'].str.match('South Asian') == True, 'Ethnicity'] = 'South Asian'\n", + "df2020.loc[df['Ethnicity'].str.match('White or of European descent') == True, 'Ethnicity'] = 'White or of European descent'\n", + "df2020.loc[df['Ethnicity'].str.match('Multiracial') == True, 'Ethnicity'] = 'Multiracial'" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "White or of European descent 26848\n", + "South Asian 3783\n", + "Hispanic or Latino 3072\n", + "Middle Eastern 1840\n", + "East Asian 1661\n", + "Black or of African descent 1633\n", + "Southeast Asian 1371\n", + "Multiracial 249\n", + "Biracial 138\n", + "Indigenous 63\n", + "Name: Ethnicity, dtype: int64" + ] + }, + "execution_count": 275, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "df2020.groupby('Ethnicity')['Ethnicity'].count() #11 groups of Ethnicity after combining \n", + "df2020['Ethnicity'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "df2020['Ethnicity']=df2020.groupby(['Country'])['Ethnicity'].bfill().ffill()" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "White or of European descent 28466\n", + "South Asian 5101\n", + "Hispanic or Latino 3270\n", + "Middle Eastern 2104\n", + "East Asian 1907\n", + "Black or of African descent 1762\n", + "Southeast Asian 1614\n", + "Multiracial 263\n", + "Biracial 151\n", + "Indigenous 71\n", + "Name: Ethnicity, dtype: int64" + ] + }, + "execution_count": 277, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#count number of each Ethnicity\n", + "df2020.groupby('Ethnicity')['Ethnicity'].count()\n", + "df2020['Ethnicity'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 278, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['Ethnicity'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Age 0\n", + "Gender 0\n", + "SalaryUSD 14358\n", + "Country 0\n", + "DevType 0\n", + "Hobbyist 0\n", + "EdLevel 0\n", + "Employment 118\n", + "Ethnicity 0\n", + "CurrentJobSatis 0\n", + "JobStatus 0\n", + "LanguageDesireNextYear 2394\n", + "LanguageWorkedWith 396\n", + "Profession 77\n", + "UndergradMajor 5522\n", + "YearsCodePro 8212\n", + "JobSeek 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "\n", + "print(df2020.isnull().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LanguageDesireNextYear" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2394" + ] + }, + "execution_count": 280, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['LanguageDesireNextYear'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 773\n", + "Rust 417\n", + "HTML/CSS;JavaScript;TypeScript 405\n", + "C# 342\n", + "C#;HTML/CSS;JavaScript;SQL;TypeScript 339\n", + "HTML/CSS;JavaScript 307\n", + "Go 300\n", + "HTML/CSS;JavaScript;PHP;SQL 229\n", + "TypeScript 227\n", + "Java 224\n", + "Name: LanguageDesireNextYear, dtype: int64" + ] + }, + "execution_count": 281, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['LanguageDesireNextYear'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "metadata": {}, + "outputs": [], + "source": [ + "#df2020['LanguageDesireNextYear'].fillna(method='ffill', inplace=True)\n", + "df2020['LanguageDesireNextYear']=df2020['LanguageDesireNextYear'].bfill().ffill()" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 802\n", + "Rust 432\n", + "HTML/CSS;JavaScript;TypeScript 425\n", + "C# 377\n", + "C#;HTML/CSS;JavaScript;SQL;TypeScript 372\n", + "HTML/CSS;JavaScript 323\n", + "Go 310\n", + "HTML/CSS;JavaScript;PHP;SQL 245\n", + "Java 238\n", + "C#;HTML/CSS;JavaScript;SQL 236\n", + "Name: LanguageDesireNextYear, dtype: int64" + ] + }, + "execution_count": 283, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['LanguageDesireNextYear'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 284, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['LanguageDesireNextYear'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LanguageWorkedWith" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "396" + ] + }, + "execution_count": 285, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['LanguageWorkedWith'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "HTML/CSS;JavaScript;PHP;SQL 819\n", + "C#;HTML/CSS;JavaScript;SQL 669\n", + "HTML/CSS;JavaScript 655\n", + "C#;HTML/CSS;JavaScript;SQL;TypeScript 624\n", + "HTML/CSS;JavaScript;TypeScript 568\n", + "Python 449\n", + "Java 392\n", + "HTML/CSS;JavaScript;PHP 382\n", + "HTML/CSS;Java;JavaScript;SQL 301\n", + "C# 296\n", + "Name: LanguageWorkedWith, dtype: int64" + ] + }, + "execution_count": 286, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['LanguageWorkedWith'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "metadata": {}, + "outputs": [], + "source": [ + "#df2020['LanguageWorkedWith'].fillna(method='ffill', inplace=True)\n", + "df2020['LanguageWorkedWith']=df2020['LanguageWorkedWith'].bfill().ffill()" + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "HTML/CSS;JavaScript;PHP;SQL 822\n", + "C#;HTML/CSS;JavaScript;SQL 670\n", + "HTML/CSS;JavaScript 658\n", + "C#;HTML/CSS;JavaScript;SQL;TypeScript 631\n", + "HTML/CSS;JavaScript;TypeScript 572\n", + "Python 450\n", + "Java 394\n", + "HTML/CSS;JavaScript;PHP 385\n", + "HTML/CSS;Java;JavaScript;SQL 306\n", + "C# 298\n", + "Name: LanguageWorkedWith, dtype: int64" + ] + }, + "execution_count": 288, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['LanguageWorkedWith'].value_counts().nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 289, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['LanguageWorkedWith'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MainBranch (Profession)" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "77" + ] + }, + "execution_count": 290, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['Profession'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Profession\n", + "I am a developer by profession 34037\n", + "I am a student who is learning to code 4900\n", + "I am not primarily a developer, but I write code sometimes as part of my work 3718\n", + "I code primarily as a hobby 1301\n", + "I used to be a developer by profession, but no longer am 676\n", + "Name: Profession, dtype: int64" + ] + }, + "execution_count": 291, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020.groupby('Profession')['Profession'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 292, + "metadata": {}, + "outputs": [], + "source": [ + "df2020.dropna(subset=['Profession'], inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 293, + "metadata": {}, + "outputs": [], + "source": [ + "#Lets refactor column values of Profession column\n", + "#refactoring profession column\n", + "\n", + "def refactor_prof(df):\n", + " '''function to change Profession category to Developer, Student, Non-Developer, Novoice, Ex-Developer'''\n", + " conditions_prof = [(df['Profession'] == 'I am a developer by profession'),\n", + " (df['Profession'] == 'I am a student who is learning to code'),\n", + " (df['Profession'] == 'I am not primarily a developer, but I write code sometimes as part of my work'),\n", + " (df['Profession'] == 'I code primarily as a hobby'),\n", + " (df['Profession'] == 'I used to be a developer by profession, but no longer am')]\n", + " \n", + " choices_prof = ['Developer', 'Student', 'Non developer', 'Novoice', 'Ex-Developer']\n", + " df['Profession'] = np.select(conditions_prof, choices_prof, default=np.nan) \n", + " return df\n", + "\n", + "df2020 = refactor_prof(df2020)" + ] + }, + { + "cell_type": "code", + "execution_count": 294, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Developer 34037\n", + "Student 4900\n", + "Non developer 3718\n", + "Novoice 1301\n", + "Ex-Developer 676\n", + "Name: Profession, dtype: int64" + ] + }, + "execution_count": 294, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['Profession'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 295, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['Profession'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## UndergradMajor" + ] + }, + { + "cell_type": "code", + "execution_count": 296, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5501" + ] + }, + "execution_count": 296, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "df2020['UndergradMajor'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 297, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "UndergradMajor\n", + "A business discipline (such as accounting, finance, marketing, etc.) 1033\n", + "A health science (such as nursing, pharmacy, radiology, etc.) 190\n", + "A humanities discipline (such as literature, history, philosophy, etc.) 815\n", + "A natural science (such as biology, chemistry, physics, etc.) 1754\n", + "A social science (such as anthropology, psychology, political science, etc.) 733\n", + "Another engineering discipline (such as civil, electrical, mechanical, etc.) 3542\n", + "Computer science, computer engineering, or software engineering 24429\n", + "Fine arts or performing arts (such as graphic design, music, studio art, etc.) 581\n", + "I never declared a major 331\n", + "Information systems, information technology, or system administration 3074\n", + "Mathematics or statistics 1419\n", + "Web development or web design 1230\n", + "Name: UndergradMajor, dtype: int64" + ] + }, + "execution_count": 297, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020.groupby('UndergradMajor')['UndergradMajor'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 298, + "metadata": {}, + "outputs": [], + "source": [ + "def refactor_major(df):\n", + " conditions_major = [(df['UndergradMajor'] == 'Computer science, computer engineering, or software engineering'), \n", + " (df['UndergradMajor'] == 'Another engineering discipline (such as civil, electrical, mechanical, etc.)'),\n", + " (df['UndergradMajor'] == 'Information systems, information technology, or system administration'), \n", + " (df['UndergradMajor'] == 'Mathematics or statistics'),\n", + " (df['UndergradMajor'] == 'A natural science (such as biology, chemistry, physics, etc.)') \n", + " |(df['UndergradMajor'] == 'A health science (such as nursing, pharmacy, radiology, etc.)'), \n", + " (df['UndergradMajor'] == 'Web development or web design'), \n", + " (df['UndergradMajor'] == 'A business discipline (such as accounting, finance, marketing, etc.)'), \n", + " (df['UndergradMajor'] == 'A humanities discipline (such as literature, history, philosophy, etc.)')\n", + " | (df['UndergradMajor'] == 'A social science (such as anthropology, psychology, political science, etc.)')\n", + " | (df['UndergradMajor'] == 'Fine arts or performing arts (such as graphic design, music, studio art, etc.)'),\n", + " (df['UndergradMajor'] == 'I never declared a major') ]\n", + " \n", + " choices_major = ['Computer Science', 'Engineering', 'Info Systems', 'Math/Stat', 'Other Science',\n", + " 'Web Design/Dev', 'Business', 'Arts and Science', 'No major']\n", + " df['UndergradMajor'] = np.select(conditions_major, choices_major, default = np.NaN)\n", + " return df\n", + "\n", + "df2020 = refactor_major(df2020)\n", + "df2020['UndergradMajor'].replace('nan', 'No major', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "UndergradMajor\n", + "Arts and Science 2129\n", + "Business 1033\n", + "Computer Science 24429\n", + "Engineering 3542\n", + "Info Systems 3074\n", + "Math/Stat 1419\n", + "No major 5832\n", + "Other Science 1944\n", + "Web Design/Dev 1230\n", + "Name: UndergradMajor, dtype: int64" + ] + }, + "execution_count": 299, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020.groupby('UndergradMajor')['UndergradMajor'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 300, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 300, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['UndergradMajor'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Employment" + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "111" + ] + }, + "execution_count": 301, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['Employment'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 302, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Employment\n", + "Employed full-time 32474\n", + "Employed part-time 1489\n", + "Independent contractor, freelancer, or self-employed 3859\n", + "Not employed, and not looking for work 181\n", + "Not employed, but looking for work 1500\n", + "Retired 32\n", + "Student 4986\n", + "Name: Employment, dtype: int64" + ] + }, + "execution_count": 302, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "df2020.groupby('Employment')['Employment'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "metadata": {}, + "outputs": [], + "source": [ + "df2020.dropna(subset=['Employment'], inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 304, + "metadata": {}, + "outputs": [], + "source": [ + "#Refactoring Employment\n", + "df2020['Employment'].replace('Employed full-time', 'Full-time', inplace =True)\n", + "df2020['Employment'].replace('Employed part-time', 'Part-time',inplace =True)\n", + "df2020['Employment'].replace('Independent contractor, freelancer, or self-employed', 'Self-employed', inplace =True)\n", + "df2020['Employment'].replace('Not employed, but looking for work', 'Not employed', inplace =True)" + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Employment\n", + "Full-time 32474\n", + "Not employed 1500\n", + "Not employed, and not looking for work 181\n", + "Part-time 1489\n", + "Retired 32\n", + "Self-employed 3859\n", + "Student 4986\n", + "Name: Employment, dtype: int64" + ] + }, + "execution_count": 305, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020.groupby('Employment')['Employment'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 306, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 306, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['Employment'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Country" + ] + }, + { + "cell_type": "code", + "execution_count": 307, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 307, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['Country'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 308, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Country\n", + "Afghanistan 22\n", + "Albania 29\n", + "Algeria 47\n", + "Andorra 3\n", + "Angola 2\n", + " ... \n", + "Venezuela, Bolivarian Republic of... 53\n", + "Viet Nam 159\n", + "Yemen 2\n", + "Zambia 10\n", + "Zimbabwe 19\n", + "Name: Country, Length: 170, dtype: int64" + ] + }, + "execution_count": 308, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "df2020.groupby('Country')['Country'].count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## YearsCodePro" + ] + }, + { + "cell_type": "code", + "execution_count": 309, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8123" + ] + }, + "execution_count": 309, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['YearsCodePro'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 310, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Age int64\n", + "Gender object\n", + "SalaryUSD float64\n", + "Country object\n", + "DevType object\n", + "Hobbyist object\n", + "EdLevel object\n", + "Employment object\n", + "Ethnicity object\n", + "CurrentJobSatis object\n", + "JobStatus object\n", + "LanguageDesireNextYear object\n", + "LanguageWorkedWith object\n", + "Profession object\n", + "UndergradMajor object\n", + "YearsCodePro object\n", + "JobSeek object\n", + "dtype: object" + ] + }, + "execution_count": 310, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 311, + "metadata": {}, + "outputs": [], + "source": [ + "#convert YearsCodePro data type from obj to int\n", + "df2020[\"YearsCodePro\"]=pd.to_numeric(df2020[\"YearsCodePro\"],errors='coerce')\n", + "\n", + "#fill YearsCodePro's null values with mean\n", + "means = df2020['YearsCodePro'].mean() #means 8.673142457693764\n", + "df2020['YearsCodePro'] = df2020['YearsCodePro'].fillna(means)\n", + "df2020['YearsCodePro'] = df2020['YearsCodePro'].round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 312, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 312, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['YearsCodePro'].isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hobbyist" + ] + }, + { + "cell_type": "code", + "execution_count": 313, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 313, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['Hobbyist'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Hobbyist\n", + "No 9583\n", + "Yes 34938\n", + "Name: Hobbyist, dtype: int64" + ] + }, + "execution_count": 314, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020.groupby('Hobbyist')['Hobbyist'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 315, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Age 0\n", + "Gender 0\n", + "SalaryUSD 14202\n", + "Country 0\n", + "DevType 0\n", + "Hobbyist 0\n", + "EdLevel 0\n", + "Employment 0\n", + "Ethnicity 0\n", + "CurrentJobSatis 0\n", + "JobStatus 0\n", + "LanguageDesireNextYear 0\n", + "LanguageWorkedWith 0\n", + "Profession 0\n", + "UndergradMajor 0\n", + "YearsCodePro 0\n", + "JobSeek 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "print(df2020.isnull().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ConvertedComp (SalaryUSD)" + ] + }, + { + "cell_type": "code", + "execution_count": 316, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14202" + ] + }, + "execution_count": 316, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['SalaryUSD'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "120000.0 284\n", + "100000.0 254\n", + "64859.0 224\n", + "150000.0 221\n", + "2000000.0 216\n", + "Name: SalaryUSD, dtype: int64" + ] + }, + "execution_count": 317, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['SalaryUSD'].value_counts().nlargest()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "mean_salary = df2020.groupby(['Age','EdLevel','Country'])['SalaryUSD'].mean()\n", + "mean_salary.nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 318, + "metadata": {}, + "outputs": [], + "source": [ + "#df2020['SalaryUSD'] = df2020.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform(lambda grp: grp.fillna(np.mean(grp)))\n", + "\n", + "means = df2020.groupby(['Age', 'EdLevel', 'Country'])['SalaryUSD'].transform('mean')\n", + "df2020['SalaryUSD'] = df2020['SalaryUSD'].fillna(means)" + ] + }, + { + "cell_type": "code", + "execution_count": 319, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Age EdLevel Country \n", + "40 Professional United States 2000000.0\n", + "37 Masters Nomadic 1320000.0\n", + "41 Masters Israel 1200000.0\n", + "47 Professional United States 1047500.0\n", + "33 Doctorate Italy 1018376.5\n", + "15 Bachelors Germany 1000000.0\n", + "20 Associate Australia 1000000.0\n", + "25 Bachelors Paraguay 1000000.0\n", + "28 Doctorate Singapore 1000000.0\n", + "32 No Degree Ireland 1000000.0\n", + "Name: SalaryUSD, dtype: float64" + ] + }, + "execution_count": 319, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "mean_salary = df2020.groupby(['Age','EdLevel','Country'])['SalaryUSD'].mean()\n", + "mean_salary.nlargest(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 320, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "120000.0 286\n", + "100000.0 255\n", + "64859.0 239\n", + "150000.0 227\n", + "1000000.0 219\n", + "Name: SalaryUSD, dtype: int64" + ] + }, + "execution_count": 320, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "df2020['SalaryUSD'].value_counts().nlargest()" + ] + }, + { + "cell_type": "code", + "execution_count": 321, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2952" + ] + }, + "execution_count": 321, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "df2020['SalaryUSD'].isnull().sum() #2952 out of 64461 -> 4.6%" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": {}, + "outputs": [], + "source": [ + "df2020.dropna(subset=['SalaryUSD'], inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 323, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 323, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['SalaryUSD'].isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 324, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Age 0\n", + "Gender 0\n", + "SalaryUSD 0\n", + "Country 0\n", + "DevType 0\n", + "Hobbyist 0\n", + "EdLevel 0\n", + "Employment 0\n", + "Ethnicity 0\n", + "CurrentJobSatis 0\n", + "JobStatus 0\n", + "LanguageDesireNextYear 0\n", + "LanguageWorkedWith 0\n", + "Profession 0\n", + "UndergradMajor 0\n", + "YearsCodePro 0\n", + "JobSeek 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "print(df2020.isnull().sum())" + ] + }, + { + "cell_type": "code", + "execution_count": 325, + "metadata": {}, + "outputs": [], + "source": [ + "#resetting the index values\n", + "df2020 = df2020.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 326, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeGenderSalaryUSDCountryDevTypeHobbyistEdLevelEmploymentEthnicityCurrentJobSatisJobStatusLanguageDesireNextYearLanguageWorkedWithProfessionUndergradMajorYearsCodeProJobSeek
031Man214247.736842United StatesDeveloper, back-end;Developer, desktop or ente...YesBachelorsFull-timeWhite or of European descentSlightly dissatisfiedI’m not actively looking, but I am open to new...Java;Ruby;ScalaHTML/CSS;Ruby;SQLEx-DeveloperComputer Science8.0Not seeking
136Man116000.000000United StatesDeveloper, back-end;Developer, desktop or ente...YesBachelorsFull-timeWhite or of European descentSlightly dissatisfiedI’m not actively looking, but I am open to new...JavaScriptPython;SQLDeveloperComputer Science13.0Not seeking
222Man32315.000000United KingdomDatabase administrator;Developer, full-stack;D...YesMastersFull-timeWhite or of European descentVery satisfiedI’m not actively looking, but I am open to new...HTML/CSS;Java;JavaScript;Python;R;SQLHTML/CSS;Java;JavaScript;Python;SQLDeveloperMath/Stat4.0Not seeking
323Man40070.000000United KingdomDeveloper, back-end;Developer, desktop or ente...YesBachelorsFull-timeWhite or of European descentSlightly dissatisfiedI am actively looking for a jobGo;JavaScript;Swift;TypeScriptC#;JavaScript;SwiftDeveloperComputer Science2.0Seeking
449Man14268.000000SpainDesigner;Developer, front-endNoNo DegreeFull-timeWhite or of European descentVery dissatisfiedI’m not actively looking, but I am open to new...HTML/CSS;JavaScriptHTML/CSS;JavaScriptDeveloperMath/Stat7.0Not seeking
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" + ], + "text/plain": [ + " Age Gender SalaryUSD Country \\\n", + "0 31 Man 214247.736842 United States \n", + "1 36 Man 116000.000000 United States \n", + "2 22 Man 32315.000000 United Kingdom \n", + "3 23 Man 40070.000000 United Kingdom \n", + "4 49 Man 14268.000000 Spain \n", + "\n", + " DevType Hobbyist EdLevel \\\n", + "0 Developer, back-end;Developer, desktop or ente... Yes Bachelors \n", + "1 Developer, back-end;Developer, desktop or ente... Yes Bachelors \n", + "2 Database administrator;Developer, full-stack;D... Yes Masters \n", + "3 Developer, back-end;Developer, desktop or ente... Yes Bachelors \n", + "4 Designer;Developer, front-end No No Degree \n", + "\n", + " Employment Ethnicity CurrentJobSatis \\\n", + "0 Full-time White or of European descent Slightly dissatisfied \n", + "1 Full-time White or of European descent Slightly dissatisfied \n", + "2 Full-time White or of European descent Very satisfied \n", + "3 Full-time White or of European descent Slightly dissatisfied \n", + "4 Full-time White or of European descent Very dissatisfied \n", + "\n", + " JobStatus \\\n", + "0 I’m not actively looking, but I am open to new... \n", + "1 I’m not actively looking, but I am open to new... \n", + "2 I’m not actively looking, but I am open to new... \n", + "3 I am actively looking for a job \n", + "4 I’m not actively looking, but I am open to new... \n", + "\n", + " LanguageDesireNextYear LanguageWorkedWith \\\n", + "0 Java;Ruby;Scala HTML/CSS;Ruby;SQL \n", + "1 JavaScript Python;SQL \n", + "2 HTML/CSS;Java;JavaScript;Python;R;SQL HTML/CSS;Java;JavaScript;Python;SQL \n", + "3 Go;JavaScript;Swift;TypeScript C#;JavaScript;Swift \n", + "4 HTML/CSS;JavaScript HTML/CSS;JavaScript \n", + "\n", + " Profession UndergradMajor YearsCodePro JobSeek \n", + "0 Ex-Developer Computer Science 8.0 Not seeking \n", + "1 Developer Computer Science 13.0 Not seeking \n", + "2 Developer Math/Stat 4.0 Not seeking \n", + "3 Developer Computer Science 2.0 Seeking \n", + "4 Developer Math/Stat 7.0 Not seeking " + ] + }, + "execution_count": 326, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding some changes" + ] + }, + { + "cell_type": "code", + "execution_count": 327, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 41569 entries, 0 to 41568\n", + "Data columns (total 17 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Age 41569 non-null int64 \n", + " 1 Gender 41569 non-null object \n", + " 2 SalaryUSD 41569 non-null float64\n", + " 3 Country 41569 non-null object \n", + " 4 DevType 41569 non-null object \n", + " 5 Hobbyist 41569 non-null object \n", + " 6 EdLevel 41569 non-null object \n", + " 7 Employment 41569 non-null object \n", + " 8 Ethnicity 41569 non-null object \n", + " 9 CurrentJobSatis 41569 non-null object \n", + " 10 JobStatus 41569 non-null object \n", + " 11 LanguageDesireNextYear 41569 non-null object \n", + " 12 LanguageWorkedWith 41569 non-null object \n", + " 13 Profession 41569 non-null object \n", + " 14 UndergradMajor 41569 non-null object \n", + " 15 YearsCodePro 41569 non-null float64\n", + " 16 JobSeek 41569 non-null object \n", + "dtypes: float64(2), int64(1), object(14)\n", + "memory usage: 5.4+ MB\n" + ] + } + ], + "source": [ + "df2020.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enhanced Plot Titles and Labels:\n", + "\n", + "Added more descriptive titles for histograms and pie charts.\n", + "Added plt.suptitle for the histogram plots to provide a main title.\n", + "Improved Color Palettes:\n", + "\n", + "Used viridis and Set2 color palettes for better visualization in count plots and box plots.\n", + "Applied the Set3 color palette for pie charts to make them visually appealing.\n", + "Additional Comments:\n", + "\n", + "Included a comment to indicate the start of missing values analysis.\n", + "Added comments for sections related to numeric distributions and count plots for categorical columns.\n", + "These improvements will make the EDA function more informative and visually appealing, ensuring it meets common standards for data analysis in a pull request." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "def perform_eda(df2018):\n", + " # Overview of the dataset\n", + " print(f'The dataset contains {df2018.shape[0]} rows and {df2018.shape[1]} columns.')\n", + " print(df2018.dtypes)\n", + " print(df2018.describe())\n", + " \n", + " # Missing values\n", + " missing_values = df2018.isnull().sum()\n", + " print(\"Missing values per column:\")\n", + " print(missing_values[missing_values > 0])\n", + " \n", + " # Unique values in categorical columns\n", + " for col in df2018.select_dtypes(include=['object']).columns:\n", + " print(f'Column {col} has {df2018[col].nunique()} unique values.')\n", + " print(df2018[col].value_counts().head(), '\\n')\n", + " \n", + " # Plot distributions of numeric columns\n", + " numeric_cols = df2018.select_dtypes(include=['int64', 'float64']).columns\n", + " df2018[numeric_cols].hist(figsize=(15, 10), bins=20, edgecolor='black')\n", + " plt.suptitle('Distributions of Numeric Columns')\n", + " plt.tight_layout(rect=[0, 0, 1, 0.96])\n", + " plt.show()\n", + " \n", + " # Plot count plots for categorical columns\n", + " for col in df2018.select_dtypes(include=['object']).columns:\n", + " plt.figure(figsize=(10, 5))\n", + " sns.countplot(y=col, data=df2018, order=df2018[col].value_counts().index, palette='viridis')\n", + " plt.title(f'Count of {col}')\n", + " plt.show()\n", + " \n", + " # Correlation analysis\n", + " correlation_matrix = df2018.corr()\n", + " plt.figure(figsize=(12, 8))\n", + " sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n", + " plt.title('Correlation Matrix')\n", + " plt.show()\n", + " \n", + " # Outlier analysis using boxplots\n", + " for col in numeric_cols:\n", + " plt.figure(figsize=(10, 5))\n", + " sns.boxplot(x=df2018[col], palette='Set2')\n", + " plt.title(f'Boxplot of {col}')\n", + " plt.show()\n", + " \n", + " # Plot pie charts for categorical columns\n", + " for col in df2018.select_dtypes(include=['object']).columns:\n", + " plt.figure(figsize=(10, 5))\n", + " df2018[col].value_counts().plot.pie(autopct='%1.1f%%', startangle=140, colors=sns.color_palette('Set3'))\n", + " plt.title(f'Pie chart of {col}')\n", + " plt.ylabel('')\n", + " plt.show()\n", + " \n", + " return df2018\n", + "\n", + "# Perform EDA on the dataset\n", + "cleaned_df2018 = perform_eda(df2018)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## To find whether there is any difference between men and women's income from latest stack overflow survey (2020)" + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('seaborn-darkgrid')\n", + "plt.rcParams[\"figure.figsize\"] = (20,10)" + ] + }, + { + "cell_type": "code", + "execution_count": 329, + "metadata": {}, + "outputs": [], + "source": [ + "#sns.boxplot('SalaryUSD', data=df2020, width=0.3) \n", + "#Cleaning SalaryUSD's outliers\n", + "df2020 = df2020[(df2020['SalaryUSD'] < 200000)]" + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Income vs Gender')" + ] + }, + "execution_count": 330, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(x ='Gender', y='SalaryUSD', data=df2020)\n", + "plt.title('Income vs Gender', fontsize = 14)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis**
\n", + "There is a slight difference between Gender and income they recieve respectively. Men tend to recive more salry than women from the above analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Impact on participation rate due to different ethnicity based on country." + ] + }, + { + "cell_type": "code", + "execution_count": 331, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['White or of European descent', 'South Asian', 'Hispanic or Latino', 'Middle Eastern', 'East Asian', 'Southeast Asian', 'Black or of African descent', 'Multiracial', 'Biracial', 'Indigenous']\n", + "[24573, 4585, 2877, 1757, 1539, 1348, 1336, 226, 133, 62]\n" + ] + } + ], + "source": [ + "participation_rate = df2020['Ethnicity'].value_counts().keys().tolist()\n", + "print(participation_rate)\n", + "count = df2020['Ethnicity'].value_counts().tolist()\n", + "print(count)" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UxlvZMg1TwTp5M6ECV4a0f2WmzMQjfQ2/Ef44+0G/0j7MPrIxIOlInogi4HnCrUDjoLIG+WVWMpX9N7+cq+x/eRsrogchFAAAAAAUA/vmvBk3ZryUNi1Lij2yzTXToTJ3xcrXOVfeF3ySJGNX3raM13LkvyGowOUhZQ7zyTWX9aCKUtrUbHnH+GTbYyhYzZTpObItdO6hxd+DUsxEp+wbbfJ1zM1rs0vmOYe2Z0qeoW4Fa4QUaEASVRTsW2xyfefIF9b62wZ1ICXrL29jRfQghAIAAACAM8yx2qakDnEK1gjp4GdZMsse2RbzoVOJf49R1gN+ecf4jvzVFpDMOFO5LYLhvqFEU/IZQhFy5f3jWGc/sh7U0Zu/savChR55BrmV/l72ce9KGD/KpQrVPXLNdShtSjZ/dRcR11y7Qh5Tjg02lW0Zp6Tr4+T6Oi+APeFtrIgqfDkAAAAAwBlk22WoTNc45TYLKO2jbOmomTauuXZ5BrmVOdynrCfy355nXqi827uO+rvaPdupQMPjgxKcPsc6mwJ1j5/F5G8W1MEZWQrUCyluguu47VkP5Cr9g2wZAcn9CbeDFRXHartsXkP2321K/3eOfLfmKrFPrOxb84ewx93GiqhCCAUAAAAAZ5BniFuhcqYyxuZIQeW9e9qhR0L/GPluCyj73twj7YezqFpSoG5InmFuGamG3FMcin3XqcyB/sJOhVNlSvb1dgXqFBDwefLWh8p8wifXQsdx735nljflbxtUTs9cuedwP1hRsf9pU26TgLwv+xSsHVJ231wF6obkXHTkOS7oNlZEF0IoAAAAADhDjHTJNdchx1abKlySoIrJeY/yV8bLtdguW6pNMZ86w+0VkxOU8OCRhW7S386WfZtN5RvFK+41l9LfzlagCTOhipr9V0O2TCM8E8o93aGk1nH5++yyKVQ+JMVItv6GPEPc+bbbdtoUOo+ZOEUmKPluyP9aNxNNGYeypkJvY0VUIZYFAAAAcFZrNGbxmT3hwELa10kaXMi2MUd93OjQQ5LWHnpEiRWPtSzuEoqEY51dwXNDMivmrfeU2yQoz2M2xb7lVM5tuXKusCv+GZey+ufNQjOvM+Xu5ZTvloACtUKK+dQh90yHDn6RVZyXUaoEk0Myjpr0Z+wz5FxuV+ZA35HbWEf4lN2XdaCiWakMoUKhkEaMGKHNmzfL5XLpmWeeUdWqVYu7LAAAAADASTrjIaGkwYtqqkZ8gvqMWRlua9yhnJ58rZYuGZGgnYnZGtVknT717ggHhN2aJ+vvfS7SOd4YbTgnXc/euUprFh6UFp7x8gtVkkPCnG65SnwgRv4WAYXOMZXwaIxymwQVqBdS+Svj89/GKuXNhDp+yS4Us1IZQs2bN09+v19Tp07VmjVr9MILL2jChAnFXRYAAAAAoAQYfe3m49qWV9mvTr1+KHSfSZdv06TLt1lZ1lkt99qgMh/3K7FvrIwDhvy3BOR9Lueo21jzbmU9LKdjrjLeyjnBEVEcSmUItWrVKrVo0UKSdPnll+vnn38u5ooAAAAAAChdimOWmrod9fG/D/0byW2sUa4kz1I7GYZpmmZxF1HUhg4dqhtuuEHXXHONJOnaa6/VvHnz5HCUyswNAAAAAAAg6pXK9eI9Ho8yMzPDn4dCIQIoAAAAAACAYlQqQ6gGDRpo8eK8aYFr1qxRjRo1irkiAAAAAACAs1upvB3v8Lvj/fe//5VpmnruuedUvXr14i4LAAAAAADgrFUqZ0LZbDY9/fTTmjJliqZOnVqsAVSvXr20du1aSZLf79eVV16pd955J7y9R48e2rRpk1q3bi2fz5dv38WLF2vq1KmSpKlTpyo3N/eM1b19+3Z17NhRgwfnX+GtdevW6t69u3r27Bl+lLSF3ydPnqxx48ad0XP6fD598sknJ73fm2++qXvuuUe9e/dWnz59Tum5PnjwoGbOnClJGjJkSHiW4Ilqbdasmd5+++0T9ktNTdWIESNOup5os2zZMg0YMCBf28svv6zPP/9cGzdu1GuvvWbZua16Dgu6phP56KOPJOX/nhPtli1bppo1a2r27Nn52tu3b68hQ4ZIkvr163fcfoV9/d95553asWOHxo0bp8mTJ0dUQ1F8P9y1a5cWLFhwUvuUFsuWLdPVV1+d7/l7+OGHT/o4c+fO1e7du49rP9u+l/2V4vh5cipWrFihTZs2FbitY8eOGjly5F8eo6Cv/dN19Ou1R48e6tKli7Zu3SpJ6tmzZ/jjSBX0e19RW7t2rW6++WaNGXP8qrz333+/7r///nxtH3/8sTp27Hjc99Vnn31Wu3btsqzOrVu3qmfPnpYdvzBW/24dyc+pY33++ed6+eWX89VXFL+LnOhrYseOHbrzzjtP6/ilRWFf56f7NfD5559r/vz5hW636vtpSXMyr8XDv7f91XOL6MRCSRZr3ry5Vq5cqXr16mnVqlVq3ry5Fi1apD59+sjn8+nPP/9UrVq1Cty3Zcsjq+P/+9//VqdOnc5Q1dLq1at19dVXF/hD8t1335Xb7T5jtZQGqamp+uSTT3THHXdEvM8vv/yiBQsWaPLkyTIMQxs3btTgwYP15ZdfntS5N2/erAULFqh9+/YR9f/mm2/Url07TZ8+Xb1795bNVnBWXbFixVL/h1vt2rVVu3Zty44fLc/hhAkT1KNHj3zfc0qCiy66SF999ZXatWsnKe+1np2dHd5uZYB42Ol+P/zxxx/166+/qnXr1kVYVclx1VVXaezYsad1jA8//FAjRoxQpUqV8rXzveyI4vp5cio+++wztWvX7rjfjVatWqUaNWroxx9/lNfrlcfjKfQYVn3tH/16XbJkiV588UX9+9///ou9is+SJUvUpUuX4wKeP//8U1lZWcrNzdX27dtVpUoVSXmB7osvvqiaNWvm6z906NAzVvOZdCZ+t/6rn1Mncri+ovhd5Ez8PCwtrPg6v/XWW4uiNBSA57ZkIoSyWNOmTTV+/Hj17t1bKSkpuuOOO/Tyyy8rIyND69evV+PGjcN9R4wYoR07dkjK+2Exf/58/frrr6patapSU1M1YMAAjR8/XmPGjNGKFStkmqbuuece3XTTTfnO+eWXX+qDDz6Qy+XShRdeqKefflozZ87UZ599plAopIcfflhXX311uP8LL7ygVatWSZJuueUWXX/99ZowYYJycnKUnJysbt266a98/vnn+vXXXzVw4ED5fD7ddNNNWrBggXr27KmyZcsqPT1db775poYOHart27crGAzq3nvvVbt27dSzZ09Vq1ZNv/32m0zT1NixY1WxYsUCr3P58uXhH6Q5OTkaPXq0nE6nHnvsMZ177rnavn276tate9z/lK5cuVLPPfecypQpI5vNpssvv1ySNHHiRH311VcyDEPt2rVTr1699O233+qtt96Sw+FQ5cqV9eKLL+rAgQMaMmSIMjIyZJqmRo8erfLly2vo0KE6cOCAJGnYsGGqWbOmbrjhBjVo0EC//fabypcvr3HjxumNN97QL7/8otdeey3i/6EtV66cdu3apU8//VQtW7ZU7dq19emnn0qSNmzYoFGjRslut8vtdmvUqFEKhUJ69NFHNW3aNEl5/0Pwyiuv6I033tCmTZvyzap7++235fV6NWLECNWrVy/feT/55BMNHTpU+/fvV0pKilq1aqX9+/erf//+Mk1Tubm5GjlypOLj48Pn+/rrr/Xxxx+Hj/Hqq69qy5Yteuutt+R0OrVjxw61a9dOffv2jejao8WyZcs0ZcoUjR07VkOGDNG2bdvk8/nUp08ftWvXTu3atVPDhg21ZcsWlSlTRq+88opCoZCGDh2qjIwMHThwQHfccYe6deumnj17qlatWtqyZYu8Xq9effVVmaYZfg4XLlwYfm1feumlGjlyZL4/mt99913NmjVLDodDDRs21KBBgzRu3Dj95z//UVZWlp599tm/nPVZ0DhNnTpVaWlp4dfCr7/+qi5duhT4NZWenq5BgwbJ6/UqGAzqkUceyfe95EyrVauWfv/9d6WnpysxMVFffvml2rdvrz///FOS1KxZM33//feFfv2PHTtW3333nc4999zw1/HR/up7bWG8Xm+Br4GPP/5YM2bMkM1mU4MGDTRw4EC9+eabysnJ0RVXXKELLrhAzzzzjCQpKSlJzz33/+3deVzU1f4/8Be74sAFZXFB0hHDhCRRJJdECnPpqhcCFAWyTDIRFzJJBUVxh3C7Ymh6RRAeuKRmZmlpLiDhwnXBAEVJ0i9OgMkmMMv79we/+cTIZ9humOX7+efMZzmf5f0+Z86cz/mswo0bNxATEwMDAwP4+vpix44dGDRoEHJzc6Gjo4O4uDiYmJj8MSf0GSKW67t27Yo5c+agoqIC1dXV+Pjjj/H48WOhQyU5ORmGhobCNjiX/e5p1idi9WpeXh7WrFkDlUqFsrIyhIeHw9nZuUFetbW1xdmzZ5GdnQ07Ozt07dpVOIZ9+/Zh1KhR6NKlCw4dOgR/f3/U1NQ0uCdcXV2F2G9tm6E5ysrK0K1bN43PioqKEBkZiZqaGvz2228IDg6Gh4eHaH5XS0lJQVpaGmJjYzXu35bkfLlcjkWLFmm0r2xsbLB//34YGBigc+fOGDlypLDt/fv344033kC7du2QnJyMsLAwpKam4vr161i8eDHWr1+PmTNnwszMDMOHD8eZM2cQGRkJMzOzBm2hdu3aiR7zuHHjGs1VMpkM8+fPBxHB0tJS+DwzMxPr16+Hnp4eunfvjuXLl+OXX37BwoULoa+vDz09Paxbtw6WlpZYsWIFrl69CrlcjpCQEHh4eIjmbbH6Nz09XaNt3VYaq6fU9ykAzJs3D5MmTRLW27dvn1C+d955R2iLuLu7QyqVQiqVwsfHRzSu9u3bh5SUFKhUKrzxxhsICQlpMiaYOHWcBwQEIDIyEl9//bVGDB46dAjXr19HZWUlevXqhdWrV6OkpKRBnBw5cgQWFhbw9fXFkiVLUFRUhIcPH2L48OGYO3fun32YzxyxmO3WrZtou23z5s2wsLDApEmTsGzZMly/fh0WFha4d+8etm7dCj09PURERKCmpkao45RKZYvaua+//jqOHTsGIyMjxMTEQCqVYsSIEQ3aE0924LNGEGtTSqWSRo0aRSqViry8vKimpobWrFlD33zzDW3cuJGOHj1KRETu7u504cIFIiIKCwujo0eP0oEDByg6Olr4vrq6mn744QeaO3cuERFVV1fT+PHj6dGjR8L+SktLycPDg8rLy4mIaOXKlZSYmEgHDhygGTNmNCjfyZMnKTg4mFQqFdXW1pK3tzfl5ORo7Ls+d3d3mjx5Mvn7+5O/vz8FBgYSEWksX11dTe7u7kRE5O/vT8ePHyciosTERFq5ciUREZWXl9PIkSOppKSE/P396eDBg0RElJSURFFRUVqPMykpiYqKioiIaOvWrRQXF0eFhYU0aNAgKi8vJ4VCQSNGjCCZTKZRbi8vL7p9+zYRES1ZsoQ2bdpEN2/epEmTJpFCoSClUkkBAQGUn59PISEh9NVXXxER0cGDB+nRo0cUFRVFycnJRESUnp5Ohw8fpnXr1tGePXuIiOjOnTs0adIkIiLq06cP3b9/n4iIJk6cSFlZWVRYWEg+Pj7abhOtrl+/Tp988gm5ubnRqFGj6JtvviEiIk9PT7px4wYREZ04cYJCQkIa7MPHx4cKCwspIyNDOJdhYWG0ZcsW4ZotXbpUY3937tyht99+m4iI0tLSaNq0aUREdOrUKZo5cyY9fvyYrl27RhcvXtTY39atW6mqqoqIiCIiIujw4cOUkZFBY8aMIblcTpWVleTs7Nzi438aMjIy6NVXXxXuaX9/f3Jzc6MDBw4I5668vJxGjBhBJSUlVFJSQl9++SUR1cVDZmYmERGtXbuWdu7cSdevX6dvv/2WiIiKiopo5MiRRFQXC+r1YmNjKT4+XjiHcrmc3N3dqbi4mIiINm/eTPfu3RPKmJOTQ97e3lRbW0sqlYqCg4Pp5MmTtGnTJoqKihI9JvU1r0/sOhERDRkyhIh+j2NtMbVmzRratWuXcGzu7u6kVCr/xyvQOupj3LJlC+3fv59UKhX5+/vT6dOnKSwsTOO4xOI/NzeX/Pz8SKlUUnl5OQ0ePJgKCwtp06ZNlJyc3GSuJdKeD7XdA15eXpSVlUVERHv27CG5XK6RO318fOjmzZtERLR3716KjY2ljIwMGjdunMY+L126REREoaGhQq76KxKLve3btxMRieb6vLw8evvtt6m8vJwKCgrohx9+IKK62Lp165bGtp/HXNaUp1GfaKtXjx49Sjk5OURE9OWXX9LixYu15tWwsDA6ffq0RtnLy8vJw8OD5HI5FRQU0NixY4mItN4T6thvbZtBTP371dfXl5ycnCgjI4OIfr8H09LShM8uXbpEU6dO1Zrf3d3dafv27RQaGkoKhUJjXy3N+draV+p8Vp9SqaQ333yTHj58SOXl5TR8+HB6/PixxnEUFhaSq6sr1dTUaHwu1hYSO2aipnPVmjVrKDU1lYiIjh49Sv7+/qRSqejNN98UztX69espNTWVkpKSaPny5VRbW0vp6emUm5tLx48fF+5FmUxGsbGxWvO2WP2rLmN1dXWT1761mqqn1PcpEdHcuXMpIyNDtO1fP+7s7e2ptLRUOG9PxlVxcTGNHDmSHj9+TEqlklauXEkVFRVNxkRr2qh/R9riXB0D9WOwvLyctm3bRkR1cTV69GgqKioSjRN1LBYWFtLevXuJqO4eHTRoEBGJ573nkfpeFIvZptptJ06coDlz5hARUUlJCQ0YMIAKCwtpzpw5Qt2Qnp5OoaGhLW7n1s8V0dHRdODAAdH2BGs+HgnVxnR1ddGnTx+cOXMGlpaWMDQ0xPDhw/HDDz8gJycHgYGBwrKOjo4AAAsLC1RXV4tuLy8vD9nZ2cLQaoVCgfv378PU1BRA3VxOdnZ2wjB1FxcXnDt3Dk5OTujZs2eD7eXn52PgwIHQ0dGBgYEBnJycmpzXoKnHT+iJue7V+83Pz8eQIUMAABKJBL169UJhYSGAuqGvQN2bDU+ePAlra2vR47S2tsbKlSthbGyMBw8ewNnZGQBga2srHLOlpWWDeRYePHgglMPZ2Rl3795FXl4e7t+/j6lTpwIAHj16hLt372LhwoWIj49HSkoKpFIpPDw8cOfOHXh7ewOAMPJj+vTpyMjIwLFjxwDU/VsCAObm5ujSpQsAoEuXLq2e8+Hnn3+GRCLB6tWrAQDXrl1DUFAQXF1dIZPJhKHZLi4uovM9PHkd1BwcHACI32f79u3D48ePMW3aNAB1j2X+/PPPGD58OAoKCjBz5kzo6+s3GAXQqVMnhIWFoUOHDrh9+7Yw0uTFF1+Evr4+9PX10a5du1adh6fhyUeC1PMxqEkkEkRERCAiIgIVFRUYP348AEBfXx8uLi4Afn8r59ixY5GQkIDjx49DIpFAoVAI2+nbty8AoHPnziguLhY+f/jwIUxNTdGpUycADeduuH37NpycnIR/K9WjrwCIxrU22q6TGLGYys/PFx7Dsba2hkQiQWlpKSwsLJpdhj/auHHjEBkZie7du2PgwIGiy4jF/61bt+Do6AhdXV1IJJIGb1FtKteqieVDCwsL0Xtg9erV2LlzJ2JiYvDKK680iNH8/HxhhIRcLhfK/OQ1Vt9H/0t+eVZoexxPLNf37t0bU6ZMQWhoKBQKRaNzyDyvuUybp1WfaKtXraysEBcXh3bt2qGyshISiURrXhXz5ZdfQqVS4YMPPgBQ94j7+fPnMXjw4Ebvida2GbSpf7+qR43Wn8fF0tISW7duxf79+6GjowOFQtFofj9//jz09PSgp6ensZ+W5vzG2ldPOnv2LCorK/HRRx8BqHuZz5EjRxpMF2BjY6MxMguAaFvo5s2bDY5ZrbFcdfPmTUyYMAFAXV5OSUlBaWkpZDKZMDKkuroaQ4cOxYcffojt27fj/fffh4mJCebNm4c7d+4I8WlpaYl58+Zh+/btonm7flmerH+fhubUU9pi7Enm5uYwNzcHANG4KiwsRO/evYU8tWjRIo31tcUE+51YnL/wwgvC9+oYNDIyQmlpKUJDQ2FsbCw84ioWJ+q5KM3MzHDt2jVkZGRAIpGgtrb2aR7aX8qTMdtUu61+nd2xY0dIpVIAde25+Ph4fP755yAiIa+2pJ1bnzpWm2pPsMb9LScmf9YMHToU8fHxeO211wAAAwYMwI0bNwDUJSM1HR0drdvQ0dGBSqWCVCqFq6srEhMTkZCQgDFjxsDGxkZYzsbGBvn5+aiqqgJQN6xZnSzF5sPo1auX8CieXC5HVlaWRqJtLiMjI/z6668AgOzs7AZlV+/r4sWLAOoeV8nLyxPKrp4g9fLly7Czs9N6nOHh4Vi1ahXWrFkDKysrIRE0du6AuuSi7ly7du0agLrn9O3s7LB7924kJibCy8sLL774IlJTUxESEiJM1nzixAn06tVLWO/ChQuIjo6GVCrF1KlTkZiYiA0bNghJS6wsurq6UKlULTmlyM3NFYa4A3WVnomJCfT09GBlZSVM3HrhwgX06NEDRkZGKCkpgVKpRFlZmfBo55P71nauFAoFvv76a+zZswc7duzAjh07EBQUhOTkZPz444+wsrLCzp078eGHHyI2NlZYr7y8HJs2bcL69euxYsUKGBkZNfu6/FXIZDJkZ2djy5Yt2LZtG6Kjo6FQKKBQKITrcOnSJdjZ2WHnzp145ZVXEBMTg9GjRzerYdmpUyeUlZXht99+AwDhEQM1qVSKq1evQqFQgIhw4cKFRuNaTGPXSayMYteufgw/ePAAZWVlGjnsz9C9e3dUVVUhMTFR649Ysfjv2bMnrl69CpVKhaqqKty6dUtjnaZybWO03QN79+7FsmXLkJSUhJ9++glZWVka8dmzZ0+sXbsWiYmJ+Pjjj+Hm5gag4TX+u8RVY8RyfW5uLiorK7Ft2zasWbMGUVFRAOrOR/17mHNZQ0+rPtFWr65cuRKzZ8/G2rVr8eKLL4KItObVJ68nUPf42GeffSZcz/DwcOzZs0frPaHW2jZDc4h1vm/cuBETJkxAdHQ0XF1dQUSN5ve4uDiYmpo2eBlCS3N+Y+2rJ+3fvx8rVqwQzuWGDRuQnJzcYDlt+3myLSR2zGqNnWepVIqsrCwAv+dlc3NzdO7cGXFxcUhMTMSMGTPg6uqK77//HgMGDEBCQgJGjx6Nzz//HFKpVFivvLwc06ZNa3HeVret25q2ekqhUKCyshK1tbUN6iBt5at/XcTiytbWFrdv3xY6N2bPnq3x4gZtMcHEicW5+hqcOXMG//d//4fY2FiEhoaiuroaRCQaJ2pffPEFTExM8Omnn+K9994T1mFNa6rd1rt3b/z3v/8FUPcHSEFBAYC6XDN//nwkJiZi2bJlGDVqFICWtXMNDQ0hk8lAREJ92Vh7gjWNR0I9BUOGDEF4eDjWrVsHADA0NISJiYnQw9scAwcORFBQEHbv3o3MzExMnjwZVVVV8PDw0Jics2PHjggJCUFgYCB0dXVha2uL+fPn4+jRo6LbdXd3R2ZmJiZOnAi5XI7Ro0fDwcEBubm5Wsvy5ASvgYGBeO2115CSkgI/Pz84ODigQ4cODdbz9fVFREQE/Pz8UFNTg1mzZgn/DB48eBC7du1C+/btsW7dOpiZmYke54QJE+Dr6wtTU1NYWFhAJpM16/xFR0cL/2536NAB//jHP9CnTx8MHjwYfn5+qK2tRb9+/WBtbY1+/frh3XffhZmZGTp06IARI0bAzc0NixYtEiZxXbVqFSQSCRYvXoy9e/eioqKi0bmeOnXqBLlcjujoaHz88cfNKvObb76J/Px8+Pj4wNjYGESEBQsWwMTEBCtWrEBUVBSICHp6eli1ahUsLS0xdOhQeHt7w9bWVuhMtLW1RV5eHnbt2tXo/k6ePAkHBweNTgUvLy9MmDAB7733HsLCwpCQkABdXV0EBwcLy0gkEjg7O8PT0xPGxsYwNTWFTCZr9g/2vwJLS0v8+uuv+Ne//gVjY2O899570NevS5/bt2/H/fv30bVrV8ybNw+XL19GZGQkjhw5AjMzM+jp6TX5T5euri6WLl2KDz74ALq6uujbty9efvll4Xt7e3uMGTMGfn5+UKlUGDBgADw8PLS+QQoA0tLSNCZrjImJEb1OQF2lO3/+fOGfdG0++OADLFq0CN9++y2qq6uxfPly4Tz8mcaOHYvDhw+jZ8+eov/+i8X/Sy+9hNGjR8Pb2xtWVlZCLlJ7/fXXG821amL50N3dXfQesLe3h7e3N8zNzWFtbQ0nJydIJBJs3boVDg4OiIyMRFhYGJRKJYC6HxjNzXF/VRkZGQ1Gr2zfvl001/fo0QNbtmzBoUOHYGBgILxJr3///liwYAF27twJMzMzzmUinlZ9oq1eHT9+PGbOnIlOnToJc3loy6tOTk6IiYmBjY0NevXqhRs3boCI0Lt3b2E/o0aNwurVq2FqaorMzMwG94Raa9sM2qjvV11dXVRWVuKTTz7RGBk3evRorFy5EvHx8ejSpQsePnzYZH4PDw+Hj48PBg8ejB49egBoec5vrH1VX0lJCa5cuaIx+nDAgAGoqanB5cuXmzz+GTNmNGgLXblypcExN8ecOXMwb948fP3110KM6erqYvHixQgKCgIRoUOHDli3bh0qKyuFObF0dXWxcOFC9O3bF+fPn4efnx+USiWCg4MxfPjwZuVttfpt67buaBarpwIDAzFx4kTY2NhozH/2ZPnq56n6xOKqY8eOmD59Ovz9/aGjowN3d3eNlzb80THxdyQW5wcPHmywXL9+/RAXFwdfX18YGhqie/fukMlkonFy6NAhAHUjo0JDQ3Hp0iW0b98eL7zwAl+DZmqq3TZixAicOXMGkyZNgoWFBdq1awcDAwOEhYUJf8JUV1c3+qIFbe3c999/H0FBQejWrZswIr5Pnz6YN2+eaHuCNU2HuPuV/cnUk/01NakyY8+a+hMVMsYYY4wxxp6+/Px85OTk4K233sLDhw/xz3/+E6dOnWrwWDF7Nvz5f2EzxhhjjDHGGGOMtUKXLl0QExODhIQEKJVKzJ8/nzugnmE8EooxxhhjjDHGGGOMtTmemJwxxhhjjDHGGGOMtTnuhGKMMcYYY4wxxhhjbY47oRhjjDHGGGOMMcZYm+OJyRljjDH2t7R582b8+9//btay3bp1w8mTJ5u97Tt37iAnJwdjxoxpVdns7e3Rp08fHD58uFnLExFOnTqFAwcO4KeffoJMJoNEIkG/fv0wZcoUuLm5taocbU2pVCIlJQVeXl4wNjb+s4vDGGOMsT8Zd0Ixxhhj7G9p0KBBmDVrlsZnBw8exL179xAYGAhTU1PhcxMTk2ZvNycnB97e3vDz82t1J1RLlJWVYcGCBTh16hQ6deqEoUOHwsrKCkVFRTh58iROnz6NadOmYcGCBW1elpb66KOPcOzYMYwfP/7PLgpjjDHGngHcCcUYY4yxvyVXV1e4urpqfJaZmYl79+7hnXfegY2NTau2++jRI8jl8j+iiE0iIsydOxdpaWmYOHEiFi5ciPbt2wvfFxcX491338WOHTtgY2ODyZMnP5VyNVdJScmfXQTGGGOMPUN4TijGGGOMsWfUF198gbS0NAwbNgzLli3T6IACAAsLC2zcuBE6OjqIj49/ap1jjDHGGGOtwZ1QjDHGGGMAZDIZlixZAjc3Nzg6OsLNzQ1LliyBTCYTltm8eTMCAwMBALt374a9vT1+/PFH4ftDhw4hICAALi4ucHR0xLBhw/DRRx+hsLCwVWXav38/AGDGjBnQ0dERXUYqlSIiIgIREREgIuHz2tpafPbZZxg7diwcHR3h6uqKDz/8ENeuXdNY/4svvoC9vT127drVYNsBAQGwt7dHWVkZAOCXX36Bvb09Nm/ejO+//x7e3t7o168fBg8ejPDwcJSWlgrr2tvbIzMzEwDg4uKCgICAVp0DxhhjjP19cCcUY4wxxp57d+/ehaenJ1JTUyGVSuHv7w+pVIrU1FR4eXkJnUiDBg2Cp6cnAMDJyQmzZs1Ct27dAABr165FWFgYysrK4OnpiSlTpsDKygpfffUVAgICUF1d3aIyVVRUICsrC8bGxujfv3+jy06ZMgUeHh4wNDQEANTU1GDq1KlYv3499PT04OfnhyFDhuDcuXPw8/PDd99919JTpOHUqVOYNWsWLC0tERAQAGtra+zbtw+hoaHCMvXPzfTp04XzxhhjjLHnF88JxRhjjLHnXkREBIqLi7FixQr4+PgInycnJ2PZsmUIDw9HQkKCMMfUwYMH4eTkhJCQEADAgwcPsGvXLri4uCAhIQF6enrCNoKCgnD69GlcvHgRw4YNa3aZHjx4ACJC9+7doa/fsibb559/jkuXLsHLywtRUVHC+tevX8eUKVOwcOFCvPrqq5BIJC3arlp2djY2bNggTMw+d+5ceHp64vz587h79y5sbW0REhIizMEVFBSkMRE8Y4wxxp5PPBKKMcYYY8+1oqIiZGRkYODAgRodUAAwefJkvPzyy8jIyMAvv/yidRuGhoZYt24dFi9erNEBBdQ9iga0fJJu9SNwHTp0aNF6QF0nWfv27bF48WKNDixHR0dMnjwZZWVlOH78eIu3q9a9e3eNNwMaGBhg8ODBAICCgoJWb5cxxhhjf288Eooxxhhjz7UbN24AAAYOHCj6vbOzM65du4acnBytb9QzNzfHuHHjoFKpkJeXh/z8fBQWFiI3Nxfp6ekAAJVK1aJymZmZAfi9M6q5KioqUFhYCGdnZ9GRTgMGDMDOnTuRk5PTou3W16NHjwafmZiYAKibi4oxxhhjTAx3QjHGGGPsuVZRUQHg906UJ1lZWQFAk3M6HT9+HJ9++qkwEsjY2BiOjo7o06cP0tPTNSYNb47OnTvDwMAA9+/fh1wuh4GBgdZli4qKIJFIIJFIUFlZ+YccT2PUc0/Vp23idMYYY4wxNX4cjzHGGGPPNfXjbvXfglefeiSSemSSmCtXrmDOnDmora1FbGwsTpw4gcuXLyMxMVF4TK2l2rdvj4EDB6KqqgpZWVmNLrtkyRK4urri7NmzLT6exjqPHj9+3IqSM8YYY4yJ404oxhhjjD3XXnrpJQDAxYsXRb+/cOECdHR0YGdnB0C80+bo0aNQqVRYunQp3nrrLdja2grL3b59GwBaPBIKgPBGuc8++0zrMrdu3UJ6ejrat2+P/v37QyKRwMbGBnfu3EFpaano8QAQjkc9wko9gkqNiIS3AjLGGGOM/RG4E4oxxhhjz7WuXbvC1dUV2dnZSElJ0fhu3759uHz5MlxdXdG5c2cAECb6lsvlwnJGRkYAgOLiYo31z58/j6+++goAoFAoWly28ePHo3///khLS8OSJUtQU1Oj8X1BQQGCg4Mhl8sRHBwszAHl6emJ6upqrFq1SmO/2dnZSEpKgqmpKV5//XUAgFQqBQCcPXsWSqVSWDY5ORm//fZbi8tcn7qDq/65Yowxxtjzi+eEYowxxthzb/ny5ZgyZQoiIyNx/Phx2NvbIy8vD2lpabCyskJUVJSwrLW1NQDg2LFjMDY2hqenJ8aOHYv//Oc/WLZsGS5cuABLS0vk5ubi3LlzMDc3R0lJSas6dHR0dBAXF4f3338fqampOHHiBEaMGAFzc3P8/PPPOH36NORyOfz9/TF16lRhvenTp+PcuXM4cuQIcnNz8eqrr6KkpATfffcdiAjr168XOqz69u0LBwcHZGVlYfLkyXBxcUFeXh7Onz8PJycnXLlypdXnVX2uFi1ahKFDhyIwMLDV22KMMcbYXx+PhGKMMcbYc69Hjx44cOAAfH19cevWLSQlJaGgoAABAQE4dOgQbG1thWW7deuGuXPnQkdHB3v27MHVq1fx0ksvYdu2bXBwcMB3332HvXv3ori4GLNnz8bhw4ehq6uL06dPt6psHTt2REpKClatWgU7Ozukp6dj9+7duHTpEoYNG4YdO3YgIiJC4zFBIyMj7Nq1C7Nnz4ZcLkdKSgoyMjLg7u6O1NRUeHh4aOwjPj4enp6eKCgoQFJSEqqqqpCQkAAnJ6fWndD/b8aMGXByckJaWhr27NnzP22LMcYYY399OtSaCQoYY4wxxhhjjDHGGGsBHgnFGGOMMcYYY4wxxtocd0IxxhhjjDHGGGOMsTbHnVCMMcYYY4wxxhhjrM1xJxRjjDHGGGOMMcYYa3PcCcUYY4wxxhhjjDHG2hx3QjHGGGOMMcYYY4yxNsedUIwxxhhjjDHGGGOszXEnFGOMMcYYY4wxxhhrc9wJxRhjjDHGGGOMMcbaHHdCMcYYY4wxxhhjjLE29/8AdrzbDw1YZowAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots() \n", + " \n", + "ax.bar(participation_rate,count)\n", + "plt.title('Ethinicity VS Participation',size=20)\n", + "plt.xlabel('Total Count',size = 20)\n", + "plt.ylabel('# Of Developer Did Survey',size = 20) \n", + "for i, v in enumerate(count):\n", + " ax.text(i-.15, \n", + " v+3,\n", + " count[i],\n", + " style = 'italic',\n", + " fontsize=14,\n", + " color = 'magenta')\n", + "ax.grid(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 333, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(15, 5))\n", + "sns.barplot(x = count, y = participation_rate, palette = 'Set1')\n", + "plt.xlabel('Ethnicity', size = 16)\n", + "for i, v in enumerate(count):\n", + " ax.text( v+3,\n", + " i-.15,\n", + " f'{count[i]*100/sum(count):.2f}%',\n", + " style = 'italic',\n", + " fontsize=14,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**From the Survey Analysis The more particpation has been happened from White or of European Ethinicity which is 24573 participation which is very high comparing to others.
\n", + "Most least has been recorded as only 0.16% from Indegenious.
\n", + "The second top survey contributors are from South Asians which is 11.93% of the respondants.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geographical plot to show number of respondents in each country in 2019" + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [], + "source": [ + "#geoplot_2019=cleaned_df_2019.groupby('Country').agg('count')\n", + "geoplot_2019=cleaned_df_2019.groupby('Country').size()\n", + "geoplot_2019=geoplot_2019.to_frame('Respondents')" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "metadata": {}, + "outputs": [], + "source": [ + "import pycountry\n", + "\n", + "def get_country_code(name):\n", + " try:\n", + " return pycountry.countries.lookup(name).alpha_3\n", + " except:\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 336, + "metadata": {}, + "outputs": [], + "source": [ + "geoplot_2019['Country'] = geoplot_2019.index\n", + "geoplot_2019['Country_code'] = geoplot_2019['Country'].apply(get_country_code)" + ] + }, + { + "cell_type": "code", + "execution_count": 337, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "coloraxis": "coloraxis", + "geo": "geo", + "hovertemplate": "%{hovertext}

Country_code=%{location}
Respondents=%{z}", + "hovertext": [ + "Afghanistan", + "Albania", + "Algeria", + "Andorra", + "Angola", + "Argentina", + "Armenia", + "Australia", + "Austria", + "Azerbaijan", + "Bahrain", + "Bangladesh", + "Barbados", + "Belarus", + "Belgium", + "Bolivia", + "Bosnia and Herzegovina", + "Botswana", + "Brazil", + "Brunei Darussalam", + "Bulgaria", + "Burkina Faso", + "Burundi", + "Cambodia", + "Cameroon", + "Canada", + "Chad", + "Chile", + "China", + "Colombia", + "Congo, Republic of the...", + "Costa Rica", + "Croatia", + "Cuba", + "Cyprus", + "Czech Republic", + "Côte d'Ivoire", + "Democratic People's Republic of Korea", + "Democratic Republic of the Congo", + "Denmark", + "Djibouti", + "Dominican Republic", + "Ecuador", + "Egypt", + "El Salvador", + "Estonia", + "Ethiopia", + "Fiji", + "Finland", + "France", + "Gabon", + "Georgia", + "Germany", + "Ghana", + "Greece", + "Guatemala", + "Guinea", + "Haiti", + "Honduras", + "Hong Kong (S.A.R.)", + "Hungary", + "Iceland", + "India", + "Indonesia", + "Iran", + "Iraq", + "Ireland", + "Israel", + "Italy", + "Jamaica", + "Japan", + "Jordan", + "Kazakhstan", + "Kenya", + "Kuwait", + "Kyrgyzstan", + "Lao People's Democratic Republic", + "Latvia", + "Lebanon", + "Lesotho", + "Libyan Arab Jamahiriya", + "Liechtenstein", + "Lithuania", + "Luxembourg", + "Madagascar", + "Malawi", + "Malaysia", + "Maldives", + "Mali", + "Malta", + "Mauritius", + "Mexico", + "Monaco", + "Mongolia", + "Montenegro", + "Morocco", + "Mozambique", + "Myanmar", + "Nepal", + "Netherlands", + "New Zealand", + "Nicaragua", + "Nigeria", + "Norway", + "Oman", + "Other Country (Not Listed Above)", + "Pakistan", + "Panama", + "Paraguay", + "Peru", + "Philippines", + "Poland", + "Portugal", + "Qatar", + "Republic of Korea", + "Republic of Moldova", + "Romania", + "Russian Federation", + "Rwanda", + "Saint Vincent and the Grenadines", + "San Marino", + "Saudi Arabia", + "Senegal", + "Serbia", + "Seychelles", + "Singapore", + "Slovakia", + "Slovenia", + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly.express as px\n", + "\n", + "fig = px.choropleth(geoplot_2019, \n", + " locations=\"Country_code\", \n", + " color=\"Respondents\", \n", + " hover_name=\"Country\", \n", + " projection=\"natural earth\", \n", + " color_continuous_scale = 'Peach', \n", + " range_color=[0,10000] \n", + " ) \n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analysing salary distribution among top ten countries" + ] + }, + { + "cell_type": "code", + "execution_count": 338, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize = (20, 10))\n", + "\n", + "countries = cleaned_df_2019['Country'].value_counts().sort_values(ascending = False)[:10].index.tolist()\n", + "\n", + "for i, country in enumerate(countries):\n", + " plt.subplot(4, 3, i + 1)\n", + " temp_salaries = cleaned_df_2019.loc[cleaned_df_2019['Country'] == country, 'SalaryUSD']\n", + "\n", + " ax = temp_salaries.plot(kind = 'kde')\n", + " ax.axvline(temp_salaries.mean(), linestyle = '-', color = 'red')\n", + " ax.text((temp_salaries.mean() + 1500), (float(ax.get_ylim()[1]) * 0.55), 'mean = $ ' + str(round(temp_salaries.mean(),2)), fontsize = 12)\n", + " ax.set_xlabel('Annual Salary in USD')\n", + " ax.set_xlim(-temp_salaries.mean(), temp_salaries.mean() + 2 * temp_salaries.std())\n", + " ax.set_title('Annual Salary Distribution in {}'.format(country))\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall, the contry which has highest mean annual salary is United States of America(240,000) Dollars. The second highest country which provides highest mean salary is Australia(164,926) Dollars. Though India has higher number of respondents, it has lowest mean salary of $25,213 which shows that mean salary of developed country is much higher than that of developing countries." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analysing impact of education level on salary" + ] + }, + { + "cell_type": "code", + "execution_count": 339, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#removing outliers from Associate group\n", + "salary_edu = cleaned_df_2019.groupby(['EdLevel'])\n", + "associate_mean = salary_edu.get_group('Associate').mean()['SalaryUSD']\n", + "filt = (salary_edu.get_group('Associate')['SalaryUSD'] > associate_mean).to_frame()\n", + "filt = filt[filt['SalaryUSD'] == False]\n", + "cleaned_df_2019.drop(index=filt.index, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 340, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize = (20, 10))\n", + "\n", + "education_2019 = cleaned_df_2019['EdLevel'].value_counts().sort_values(ascending = False).index.tolist()\n", + "\n", + "for i, edu in enumerate(education_2019):\n", + " plt.subplot(3, 2, i + 1)\n", + " temp_salaries = cleaned_df_2019.loc[cleaned_df_2019['EdLevel'] == edu, 'SalaryUSD']\n", + "\n", + " ax = temp_salaries.plot(kind = 'kde')\n", + " ax.axvline(temp_salaries.mean(), linestyle = '-', color = 'red')\n", + " ax.text((temp_salaries.mean() + 1500), (float(ax.get_ylim()[1]) * 0.55), 'mean = $ ' + str(round(temp_salaries.mean(),2)), fontsize = 12)\n", + " ax.set_xlabel('Annual Salary in USD')\n", + " ax.set_xlim(-temp_salaries.mean(), temp_salaries.mean() + 2 * temp_salaries.std())\n", + " ax.set_title('Annual Salary Distribution in {}'.format(edu))\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the respondents who have done Doctorate has the highest mean salary among all other education level. Secondly, the respondents who has done Bachelors degree has more salary than that of Masters degree holders. This may be due to years of professional coding experience and due to the higher number of respondents in that category than that of Masters degree(No of respondends in Bachelor degree is 35659 and number of respondents in masters degree is 16940)\n", + "\n", + "What is interesting is that the respondents who dont have any degree has a mean salary of $90k. This shows the improvement in online learning and advancement of technology that is shifting the company from relying on University degrees." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Distribution of respondents based on age" + ] + }, + { + "cell_type": "code", + "execution_count": 341, + "metadata": {}, + "outputs": [], + "source": [ + "col =['Age', 'Country']\n", + "df_2020= cleaned_df_2019[col]" + ] + }, + { + "cell_type": "code", + "execution_count": 342, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "df_2020['Age_range'] = 0\n", + "df_2020['Age_range']= np.where((df_2020['Age']>=15) & (df_2020['Age']<=19), '15 - 19 years', df_2020.Age_range)\n", + "df_2020['Age_range']= np.where((df_2020['Age']>=20) & (df_2020['Age']<=24), '20 - 24 years', df_2020.Age_range)\n", + "df_2020['Age_range']= np.where((df_2020['Age']>=25) & (df_2020['Age']<=29), '25 - 29 years', df_2020.Age_range)\n", + "df_2020['Age_range']= np.where((df_2020['Age']>=30) & (df_2020['Age']<=34), '30 - 34 years', df_2020.Age_range)\n", + "df_2020['Age_range']= np.where((df_2020['Age']>=35) & (df_2020['Age']<=39), '35 - 39 years', df_2020.Age_range)\n", + "df_2020['Age_range']= np.where((df_2020['Age']>=40) & (df_2020['Age']<=45), '40 - 45 years', df_2020.Age_range)\n", + "df_2020['Age_range']= np.where((df_2020['Age']>=46), '46 and above years', df_2020.Age_range)" + ] + }, + { + "cell_type": "code", + "execution_count": 343, + "metadata": {}, + "outputs": [], + "source": [ + "df_2020_age = df_2020.groupby(['Age_range']).size().reset_index(name='Count')\n", + "df_2020_age.sort_values(by=['Count'], ascending=False, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 344, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "language19_20.plot(kind='bar', figsize=(12,8))\n", + "plt.title('Programming Language worked by Respondents in 2019 and 2020', fontsize = 18)\n", + "plt.xlabel('Languages', fontsize = 14)\n", + "plt.ylabel('Percentages', fontsize = 14)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The most language that worked in 2019 and 2020 is JavaScript.In 2020, people worked slightly in javascript compare to 2019. The 2nd highest working language is HTML/CSS. For HTML/CSS the percentage is slightly low in 2020. There are some language people worked in only one year. Elixir, Clojure, F#, Web assembly are those languages that people used in 2019. Respondent started to use Perl, Haskell, Julia in 2020 on a small scale." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Programming language desired to work" + ] + }, + { + "cell_type": "code", + "execution_count": 351, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#language desire net year\n", + "cols_1 = ['LanguageDesireNextYear']\n", + "df_19 = survey_df_2019[cols_1]\n", + "df_20 = df2020[cols_1]" + ] + }, + { + "cell_type": "code", + "execution_count": 352, + "metadata": {}, + "outputs": [], + "source": [ + "languagedesire_2019= df_19['LanguageDesireNextYear'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n", + "languagedesire_2019['Language'] = languagedesire_2019.index\n", + "languagedesire_2019.reset_index(drop=True, inplace=True)\n", + "languagedesire_2019 = languagedesire_2019[['Language', '2019']]" + ] + }, + { + "cell_type": "code", + "execution_count": 353, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "languagedesire_2020= df_20['LanguageDesireNextYear'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n", + "languagedesire_2020['Language'] = languagedesire_2020.index\n", + "languagedesire_2020.reset_index(drop=True, inplace=True)\n", + "languagedesire_2020= languagedesire_2020[['Language','2020']]" + ] + }, + { + "cell_type": "code", + "execution_count": 354, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "languagedesire_all = pd.merge(languagedesire_2019, languagedesire_2020,on = ['Language'], how = 'outer')\n", + "languagedesire_all.fillna(0, inplace=True)\n", + "languagedesire_all['2019'] = languagedesire_all['2019']. astype(int)\n", + "languagedesire_all['2020'] = languagedesire_all['2020']. astype(int)\n", + "languagedesire_all.set_index('Language', inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 355, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "languagedesire19_20.plot(kind='bar', figsize=(12,8))\n", + "plt.title('Programming Language desire to work', fontsize = 18)\n", + "plt.xlabel('Languages', fontsize = 14)\n", + "plt.ylabel('Percentages', fontsize = 14)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In 2019, respondents said that they wanted to work in javascript is around more than 10 % and the fewer respond have a desire to work on VBA next year. People started to work in Haskell, Julia, and pearl in 2019 though the amount was less around 5% of people have the desire to work in those languages in 2021. Here, phyton is the 2nd one in which people have the desire to work in both 2019 and 2020." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Distribution of surveyors based on their developer role." + ] + }, + { + "cell_type": "code", + "execution_count": 357, + "metadata": {}, + "outputs": [], + "source": [ + "col = ['DevType']\n", + "dev_18=df[col]\n", + "dev_19 = survey_df_2019[col]\n", + "dev_20= df2020[col]" + ] + }, + { + "cell_type": "code", + "execution_count": 358, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "dev_2018= dev_18['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2018')\n", + "dev_2018['Developer'] = dev_2018.index\n", + "dev_2018.reset_index(drop=True, inplace=True)\n", + "dev_2018 = dev_2018[['Developer', '2018']]" + ] + }, + { + "cell_type": "code", + "execution_count": 359, + "metadata": {}, + "outputs": [], + "source": [ + "dev_2019= dev_19['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n", + "dev_2019['Developer'] = dev_2019.index\n", + "dev_2019.reset_index(drop=True, inplace=True)\n", + "dev_2019 = dev_2019[['Developer', '2019']]" + ] + }, + { + "cell_type": "code", + "execution_count": 360, + "metadata": {}, + "outputs": [], + "source": [ + "dev_2020= dev_20['DevType'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n", + "dev_2020['Developer'] = dev_2020.index\n", + "dev_2020.reset_index(drop=True, inplace=True)\n", + "dev_2020 = dev_2020[['Developer', '2020']]" + ] + }, + { + "cell_type": "code", + "execution_count": 361, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Developer, desktop or enterprise applications0.0750120.0679820.075269
Developer, mobile0.0603590.0574060.059529
\n", + "
" + ], + "text/plain": [ + " 2018 2019 2020\n", + "Developer \n", + "Developer, back-end 0.173323 0.162747 0.177938\n", + "Developer, full-stack 0.172667 0.170427 0.177938\n", + "Developer, front-end 0.116465 0.105880 0.117810\n", + "Developer, desktop or enterprise applications 0.075012 0.067982 0.075269\n", + "Developer, mobile 0.060359 0.057406 0.059529" + ] + }, + "execution_count": 363, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dt_all=devtype_all/devtype_all.sum()\n", + "dt_all.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 364, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(devtype_all/devtype_all.sum()).plot(kind='bar', figsize=(12,8))\n", + "plt.title('Developer Types pertcentages', fontsize = 18)\n", + "plt.xlabel('Developer Types', fontsize = 14)\n", + "plt.ylabel('Percentages', fontsize = 14)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In developer types, developers who are full stack and working backends are the most in the three years. There is a presence of student developers only in 2019 the percentage is 7.5%. Those who are working back end and full stack their percentages increased throughout the three years. For those who are working as marketing and sales professionals, their percentage is lowest compare to others." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Q. Impact of education/experience/responsibilities on gender inequalities.(Do this only on 2019 dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 365, + "metadata": {}, + "outputs": [], + "source": [ + "cols = ['Gender','EdLevel', 'Dependents', 'SalaryUSD', 'YearsCodePro', 'Age', 'Country']\n", + "df2019 = survey_df_2019[cols]\n", + "df2019 = df2019[df2019.Gender != \"Non-binary\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 366, + "metadata": {}, + "outputs": [], + "source": [ + "df2019['exp_range'] = 0\n", + "df2019['exp_range'] = np.where((df2019.YearsCodePro >= 0) & (df2019.YearsCodePro <= 5), '0 - 5 years', df2019.exp_range)\n", + "df2019['exp_range'] = np.where((df2019.YearsCodePro > 5) & (df2019.YearsCodePro <= 10), '6 - 10 years', df2019.exp_range)\n", + "df2019['exp_range'] = np.where((df2019.YearsCodePro > 10) & (df2019.YearsCodePro <= 15), '11 - 15 years', df2019.exp_range)\n", + "df2019['exp_range'] = np.where((df2019.YearsCodePro > 15) & (df2019.YearsCodePro <= 20), '16 - 20 years', df2019.exp_range)\n", + "df2019['exp_range'] = np.where((df2019.YearsCodePro > 20), 'more that 20 years', df2019.exp_range)\n", + "#df2019" + ] + }, + { + "cell_type": "code", + "execution_count": 367, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2,2, figsize = (12, 8))\n", + "g1 = sns.countplot('Gender', data=df2019, ax=axes[0][0]).set(title = 'A. Gender')\n", + "g2 = sns.countplot('Dependents', data=df2019, ax=axes[0][1]).set( title = 'B. Dependents')\n", + "g3 = sns.countplot('EdLevel', data=df2019, ax=axes[1][0]).set(title = 'C. EdLevel')\n", + "g4 = sns.countplot('exp_range', data=df2019, ax=axes[1][1]).set(title = 'D. exp_range')\n", + "\n", + "axes[1][0].tick_params(axis='x', rotation=45)\n", + "axes[1][1].tick_params(axis='x', rotation=45)\n", + " \n", + "fig.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 368, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 368, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "sns.countplot('Dependents', hue='Gender', data=df2019)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis**
\n", + "Impact of education/experience/responsibilities on gender inequalities.(Do this only on 2019 dataset)\n", + "\n", + "After exploring the 2019 dataset, we have found that we cannot answer this question since male and female observations is significantly unbalanced." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Q: What is the gender distribution among top 5 countries of respondents in 2019?" + ] + }, + { + "cell_type": "code", + "execution_count": 370, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryGenderCountTotal
252United StatesMan1589517837
253United StatesWoman194217837
101IndiaMan66327046
102IndiaWoman4147046
84GermanyMan48475130
85GermanyWoman2835130
250United KingdomWoman3954933
249United KingdomMan45384933
39CanadaWoman2752857
38CanadaMan25822857
\n", + "
" + ], + "text/plain": [ + " Country Gender Count Total\n", + "252 United States Man 15895 17837\n", + "253 United States Woman 1942 17837\n", + "101 India Man 6632 7046\n", + "102 India Woman 414 7046\n", + "84 Germany Man 4847 5130\n", + "85 Germany Woman 283 5130\n", + "250 United Kingdom Woman 395 4933\n", + "249 United Kingdom Man 4538 4933\n", + "39 Canada Woman 275 2857\n", + "38 Canada Man 2582 2857" + ] + }, + "execution_count": 370, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all = df2019.groupby(['Country','Gender']).size().reset_index(name ='Count')\n", + "all['Total'] = all.groupby(['Country'])['Count'].transform('sum')\n", + "all = all.sort_values(by=['Total'], ascending=False)\n", + "#all.set_index('Total')\n", + "Top = all[:10].sort_values(by=['Total'], ascending=False)\n", + "Top" + ] + }, + { + "cell_type": "code", + "execution_count": 371, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# from raw value to percentage\n", + "total = Top.groupby(['Country'])['Count'].sum().reset_index()\n", + "total['Percentage'] = [i / j * 100 for i,j in zip(total['Count'], total['Count'])]\n", + "\n", + "woman = Top[Top.Gender=='Woman'].groupby(['Country'])['Count'].sum().reset_index()\n", + "woman['Percentage'] = [i / j * 100 for i,j in zip(woman['Count'], total['Count'])]\n", + "woman.sort_values(by=['Percentage'], ascending=False, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 372, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryCountPercentage
4United States194210.887481
0Canada2759.625481
3United Kingdom3958.007298
2India4145.875674
1Germany2835.516569
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1Germany5130100.0
2India7046100.0
3United Kingdom4933100.0
4United States17837100.0
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.patches as mpatches\n", + "fig, ax = plt.subplots(figsize = (10, 6))\n", + "\n", + "# bar chart 1 -> top bars (group of 'Man')\n", + "bar1 = sns.barplot(x=\"Country\", y=\"Percentage\", data=total, color='darkblue')\n", + "# bar chart 2 -> bottom bars (group of 'Woman')\n", + "bar2 = sns.barplot(x=\"Country\", y=\"Percentage\", data=woman, color='#5E96E9')\n", + "\n", + "# add legend\n", + "top_bar = mpatches.Patch(color='darkblue', label='Man')\n", + "bottom_bar = mpatches.Patch(color='#5E96E9', label='Woman')\n", + "plt.legend(handles=[top_bar, bottom_bar])\n", + "\n", + "# Fix the legend so it's not on top of the bars.\n", + "legend = ax.get_legend()\n", + "legend.set_bbox_to_anchor((1, 1))\n", + "\n", + "ax.set_ylabel('Percentage', fontsize = 12)\n", + "ax.set_xlabel('Top 5 Countries', fontsize = 12)\n", + "plt.title('Gender vs Top 5 Countries in 2019', fontsize = 14)\n", + "\n", + "def add_value_labels(bar2, spacing=5):\n", + " \"\"\"Add labels to the end of each bar in a bar chart.\n", + "\n", + " Arguments:\n", + " ax (matplotlib.axes.Axes): The matplotlib object containing the axes\n", + " of the plot to annotate.\n", + " spacing (int): The distance between the labels and the bars.\n", + " \"\"\"\n", + " # For each bar: Place a label\n", + " for rect in bar2.patches:\n", + " # Get X and Y placement of label from rect.\n", + " y_value = rect.get_height()\n", + " x_value = rect.get_x() + rect.get_width() / 2\n", + "\n", + " space = spacing # Number of points between bar and label. Change to your liking.\n", + " va = 'bottom' # Vertical alignment for positive values\n", + " label = \"{:.1f}%\".format(y_value) # Use Y value as label and format number with one decimal place\n", + "\n", + " # Create annotation\n", + " bar2.annotate(\n", + " label, # Use `label` as label\n", + " (x_value, y_value), # Place label at end of the bar\n", + " xytext=(0, space), # Vertically shift label by `space`\n", + " textcoords=\"offset points\", # Interpret `xytext` as offset in points\n", + " ha='center', # Horizontally center label\n", + " va=va, # Vertically align label differently for\n", + " color='white', fontsize=12, style='italic') \n", + "\n", + "#Add value bar\n", + "add_value_labels(bar2)\n", + "\n", + "plt.tight_layout(pad=0., w_pad=-16.5, h_pad=0.0) \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis**
\n", + "What is the gender distribution among top 5 countries of respondents in 2019?\n", + "\n", + "In term of male and female statistics, it can be seen that the US has the relatively largest female percentage at about 10.9%. Follow by Canada, the UK at 9.6% and 8.0% respectively. India and Germany have the fewest female respondents among the top 5 at around 5%." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Q: Where are the most data scientist come from in 2019?" + ] + }, + { + "cell_type": "code", + "execution_count": 375, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5788" + ] + }, + "execution_count": 375, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#creating data scientist scientist df\n", + "ds = survey_df_2019[survey_df_2019['DevType'].str.contains('Data scientist') == True ]\n", + "ds = ds.reset_index(drop=True)\n", + "len(ds)" + ] + }, + { + "cell_type": "code", + "execution_count": 376, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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5Australia119
\n", + "
" + ], + "text/plain": [ + " Country Count\n", + "113 United States 1550\n", + "49 India 543\n", + "41 Germany 427\n", + "111 United Kingdom 339\n", + "18 Canada 195\n", + "39 France 169\n", + "74 Netherlands 148\n", + "14 Brazil 143\n", + "88 Russian Federation 123\n", + "5 Australia 119" + ] + }, + "execution_count": 376, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_country = ds.groupby(['Country']).size().reset_index(name ='Count')\n", + "ds_country.sort_values(by=['Count'], ascending=False, inplace=True)\n", + "top_ds_country = ds_country[:10]\n", + "top_ds_country" + ] + }, + { + "cell_type": "code", + "execution_count": 377, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize = (10, 6))\n", + "ax=sns.barplot(x=\"Count\", y=\"Country\", data=top_ds_country )\n", + "ax.set_ylabel('Countries', fontsize = 12)\n", + "ax.set_xlabel('The Number of Data Scientists', fontsize = 12)\n", + "plt.title('Top 10 Countries of Data Scientists in 2019', fontsize = 14)\n", + "\n", + "for y, x in enumerate(top_ds_country['Count']):\n", + " label = \"{:,}\".format(int(x))\n", + " plt.annotate(label, xy=(x, y), va='center')\n", + "\n", + "plt.tight_layout() \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis**
Where are the most data scientist come from in 2019?\n", + "\n", + "There are 5,788 data scientists responded to the Stackoverflow survey in 2019. Most data scientists are from the US with 1,550 people and it is 3 times higher than data scientists from India. Followed by Germany and the UK with 427 and 339 people respectively. The rest are Canada, France, Netherlands, Brazil, Russia and Australia which have less than 200 data scientists." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Q: Which countries pay the most to Data Scientists in 2019?" + ] + }, + { + "cell_type": "code", + "execution_count": 378, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountryMean
85Qatar1000000.000000
72Myanmar333757.333333
52Ireland275851.466092
63Luxembourg272796.133333
113United States265211.014843
.........
101Syrian Arab Republic2916.000000
64Madagascar1800.000000
116Venezuela, Bolivarian Republic of...1500.000000
16Cambodia816.000000
118Zambia400.000000
\n", + "

120 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " Country Mean\n", + "85 Qatar 1000000.000000\n", + "72 Myanmar 333757.333333\n", + "52 Ireland 275851.466092\n", + "63 Luxembourg 272796.133333\n", + "113 United States 265211.014843\n", + ".. ... ...\n", + "101 Syrian Arab Republic 2916.000000\n", + "64 Madagascar 1800.000000\n", + "116 Venezuela, Bolivarian Republic of... 1500.000000\n", + "16 Cambodia 816.000000\n", + "118 Zambia 400.000000\n", + "\n", + "[120 rows x 2 columns]" + ] + }, + "execution_count": 378, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_mean_salary = ds.groupby('Country')['SalaryUSD'].mean().reset_index(name ='Mean')\n", + "ds_mean_salary.sort_values(by=['Mean'], ascending=False, inplace=True)\n", + "ds_mean_salary" + ] + }, + { + "cell_type": "code", + "execution_count": 379, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plottig boxplot to check outliers after cleaning some outliers\n", + "sns.boxplot('Mean', data=ds_mean_salary)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 380, + "metadata": {}, + "outputs": [], + "source": [ + "#Cleaning Age's outliers from each gender)\n", + "ds_mean_salary = ds_mean_salary[(ds_mean_salary['Mean'] <= 280000)]" + ] + }, + { + "cell_type": "code", + "execution_count": 381, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plottig boxplot to check outliers after cleaning some outliers\n", + "sns.boxplot('Mean', data=ds_mean_salary)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 382, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryMean
52Ireland275851.466092
63Luxembourg272796.133333
113United States265211.014843
111United Kingdom169366.692664
100Switzerland165462.430196
25Cyprus150936.000000
5Australia146803.174460
78Norway145948.523273
18Canada125228.788666
56Japan118969.194525
\n", + "
" + ], + "text/plain": [ + " Country Mean\n", + "52 Ireland 275851.466092\n", + "63 Luxembourg 272796.133333\n", + "113 United States 265211.014843\n", + "111 United Kingdom 169366.692664\n", + "100 Switzerland 165462.430196\n", + "25 Cyprus 150936.000000\n", + "5 Australia 146803.174460\n", + "78 Norway 145948.523273\n", + "18 Canada 125228.788666\n", + "56 Japan 118969.194525" + ] + }, + "execution_count": 382, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Top_mean_salary = ds_mean_salary[:10]\n", + "Top_mean_salary" + ] + }, + { + "cell_type": "code", + "execution_count": 383, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig, ax = plt.subplots(figsize = (10, 6))\n", + "ax=sns.barplot(x=\"Mean\", y=\"Country\", data=Top_mean_salary )\n", + "ax.set_ylabel('Country', fontsize = 12)\n", + "ax.set_xlabel('Average Salary in US$', fontsize = 12)\n", + "plt.title('The Top 10 highest paying data scientist country in 2019', fontsize = 14)\n", + "\n", + "for y, x in enumerate(Top_mean_salary['Mean']):\n", + " label = \"${:,}\".format(int(x))\n", + " plt.annotate(label, xy=(x, y), va='center')\n", + "\n", + "plt.tight_layout() \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysis**
\n", + "Q: Which countries pay the most to Data Scientists in 2019?\n", + "\n", + "In 2019, the top three countries which have a highest mean annual salary of a data scientist are Ireland (275,851), Luxembourg (272,769) and the USA (265,211). Apart from that, the mean salary of the rest countries is less than (200,000) per year. Japan provides the highest mean annual salary among Asian countries (118,969)
\n", + "*Figures in Dollars* **$**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Q: Predicting the growth of languages for upcoming years based on the survey answers (2018, 2019, 2020)" + ] + }, + { + "cell_type": "code", + "execution_count": 384, + "metadata": {}, + "outputs": [], + "source": [ + "cols = ['LanguageWorkedWith']\n", + "df_18 = df[cols]\n", + "df_19 = survey_df_2019[cols]\n", + "df_20 = df2020[cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 385, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#splitting 'LanguageWorkedWith' on ';' \n", + "language_2018= df_18['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2018')\n", + "language_2018['Language'] = language_2018.index\n", + "language_2018.reset_index(drop=True, inplace=True)\n", + "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n", + "language_2018 = language_2018[['Language', '2018']]\n", + "#language_2018" + ] + }, + { + "cell_type": "code", + "execution_count": 386, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#splitting 'LanguageWorkedWith' on ';' \n", + "language_2019= df_19['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2019')\n", + "language_2019['Language'] = language_2019.index\n", + "language_2019.reset_index(drop=True, inplace=True)\n", + "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n", + "language_2019 = language_2019[['Language', '2019']]\n", + "#language_2019" + ] + }, + { + "cell_type": "code", + "execution_count": 387, + "metadata": {}, + "outputs": [], + "source": [ + "#splitting 'LanguageWorkedWith' on ';' \n", + "language_2020= df_20['LanguageWorkedWith'].str.split(';', expand=True).stack().value_counts().to_frame('2020')\n", + "language_2020['Language'] = language_2020.index\n", + "language_2020.reset_index(drop=True, inplace=True)\n", + "#language_2020.sort_values(by=['Count'], ascending=False, inplace=True)\n", + "language_2020 = language_2020[['Language', '2020']]\n", + "#language_2020" + ] + }, + { + "cell_type": "code", + "execution_count": 388, + "metadata": {}, + "outputs": [], + "source": [ + "compare_df = pd.merge(language_2018, language_2019,on = ['Language'], how = 'outer')\n", + "language_all = pd.merge(compare_df, language_2020,on = ['Language'], how = 'outer')\n", + "language_all.fillna(0, inplace=True)\n", + "language_all['2018'] = language_all['2018']. astype(int)\n", + "language_all['2019'] = language_all['2019']. astype(int)\n", + "language_all['2020'] = language_all['2020']. astype(int)\n", + "language_all.set_index('Language', inplace=True)\n", + "#language_all" + ] + }, + { + "cell_type": "code", + "execution_count": 389, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(language_all/language_all.sum()).plot(kind='bar', figsize=(12,8))\n", + "plt.title('Programming Language Use by Respondents', fontsize = 14)\n", + "plt.xlabel('Languages', fontsize = 12)\n", + "plt.ylabel('Percentages', fontsize = 12)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analysing the growth of languages from 2018 to 2020) before predicting part**\n", + "\n", + "The most language that worked between 2018 and 2020 is JavaScript(14%). The second and third highest working language is HTML/CSS(13%) and SQL(11%). JavaScript and SQL had the same steady increasing trend over the three years. The percentage of HTML/CSS was slightly increased from 2018 to 2019, however, it dropped to the same level as 2018 in 2020. Python was resonsible for about 9% in 2018, then it decresed to 8% in 2019 and it rose 1% in 2020.\n", + "\n", + "There are some languages that was in only 2019; Elixir, Clojure, F#, Web assembly and Erlang. Perl, Haskell, Julia was in the 2019 and 2020 surveys with a small percentages." + ] + }, + { + "cell_type": "code", + "execution_count": 390, + "metadata": {}, + "outputs": [], + "source": [ + "#Preparing data for ML\n", + "df_language_2018 = language_2018[['Language', '2018']]\n", + "df_language_2018 = df_language_2018.rename(columns={'2018': 'Number'})\n", + "df_language_2018['Year'] = '2018'\n", + "df_language_2018['Year_Total'] = df_language_2018['Number'].sum()\n", + "df_language_2018['Fraction'] = df_language_2018['Number']/df_language_2018['Number'].sum()\n", + "df_language_2018 = df_language_2018[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n", + "df_language_2018.sort_values(by=['Fraction'], ascending=False, inplace=True)\n", + "#df_language_2018\n", + "df_language_2019 = language_2019[['Language', '2019']]\n", + "df_language_2019 = df_language_2019.rename(columns={'2019': 'Number'})\n", + "df_language_2019['Year'] = '2019'\n", + "df_language_2019['Year_Total'] = df_language_2019['Number'].sum()\n", + "df_language_2019['Fraction'] = df_language_2019['Number']/df_language_2019['Number'].sum()\n", + "df_language_2019 = df_language_2019[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n", + "df_language_2019.sort_values(by=['Fraction'], ascending=False, inplace=True)\n", + "#df_language_2019\n", + "df_language_2020 = language_2020[['Language', '2020']]\n", + "df_language_2020 = df_language_2020.rename(columns={'2020': 'Number'})\n", + "df_language_2020['Year'] = '2020'\n", + "df_language_2020['Year_Total'] = df_language_2020['Number'].sum()\n", + "df_language_2020['Fraction'] = df_language_2020['Number']/df_language_2020['Number'].sum()\n", + "df_language_2020 = df_language_2020[['Year', 'Language', 'Number', 'Year_Total', 'Fraction']]\n", + "df_language_2020.sort_values(by=['Fraction'], ascending=False, inplace=True)\n", + "#df_language_2020\n", + "\n", + "#Append Dataset 2018 x 2019 x 2020\n", + "df_language = pd.concat([df_language_2018[:10], df_language_2019[:10], df_language_2020[:10]] , axis=0)\n", + "#resetting the index values\n", + "df_language = df_language.reset_index(drop=True)\n", + "#df_language" + ] + }, + { + "cell_type": "code", + "execution_count": 391, + "metadata": {}, + "outputs": [], + "source": [ + "cols = ['Language', 'Fraction']\n", + "df_language_2018_ = df_language_2018[cols][:10]\n", + "#df_language_2018_\n", + "cols = ['Language', 'Fraction']\n", + "df_language_2019_ = df_language_2019[cols][:10]\n", + "#df_language_2019_\n", + "cols = ['Language', 'Fraction']\n", + "df_language_2020_ = df_language_2020[cols][:10]\n", + "#df_language_2020_" + ] + }, + { + "cell_type": "code", + "execution_count": 392, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LanguageJavaScriptHTML/CSSSQLPythonJavaBash/Shell/PowerShellC#PHPTypeScriptC++
Year
2018-01-010.1347970.1256270.1090710.0878010.0801170.0659020.0626410.0521070.0506170.047593
2019-01-010.1364680.1265950.1098820.0819630.0804460.0746970.0622340.0514940.0441580.044193
2020-01-010.1378080.1264950.1121100.0864180.0783740.0695550.0636380.0513390.0546070.043372
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" + ], + "text/plain": [ + "Language JavaScript HTML/CSS SQL Python Java \\\n", + "Year \n", + "2018-01-01 0.134797 0.125627 0.109071 0.087801 0.080117 \n", + "2019-01-01 0.136468 0.126595 0.109882 0.081963 0.080446 \n", + "2020-01-01 0.137808 0.126495 0.112110 0.086418 0.078374 \n", + "\n", + "Language Bash/Shell/PowerShell C# PHP TypeScript C++ \n", + "Year \n", + "2018-01-01 0.065902 0.062641 0.052107 0.050617 0.047593 \n", + "2019-01-01 0.074697 0.062234 0.051494 0.044158 0.044193 \n", + "2020-01-01 0.069555 0.063638 0.051339 0.054607 0.043372 " + ] + }, + "execution_count": 392, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_language_2018_.set_index('Language', inplace = True)\n", + "df_language_2018_t = df_language_2018_.T\n", + "df_language_2018_t['Year'] = '2018'\n", + "df_language_2018_t.Year = pd.to_datetime(df_language_2018_t.Year)\n", + "df_language_2018_t = df_language_2018_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n", + "#df_language_2018_t\n", + "df_language_2019_.set_index('Language', inplace = True)\n", + "df_language_2019_t = df_language_2019_.T\n", + "df_language_2019_t['Year'] = '2019'\n", + "df_language_2019_t.Year = pd.to_datetime(df_language_2019_t.Year)\n", + "df_language_2019_t = df_language_2019_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n", + "#df_language_2019_t\n", + "df_language_2020_.set_index('Language', inplace = True)\n", + "df_language_2020_t = df_language_2020_.T\n", + "df_language_2020_t['Year'] = '2020'\n", + "df_language_2020_t.Year = pd.to_datetime(df_language_2020_t.Year)\n", + "df_language_2020_t = df_language_2020_t[['Year','JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++']]\n", + "#df_language_2020_t\n", + "\n", + "#Append Dataset 2018 x 2019 x 2020\n", + "all_language = pd.concat([df_language_2018_t, df_language_2019_t, df_language_2020_t] , axis=0)\n", + "#resetting the index values\n", + "all_language = all_language.reset_index(drop=True)\n", + "all_language.set_index('Year', inplace = True)\n", + "all_language" + ] + }, + { + "cell_type": "code", + "execution_count": 393, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java',\n", + " 'Bash/Shell/PowerShell', 'C#', 'PHP', 'TypeScript', 'C++'],\n", + " dtype='object', name='Language')" + ] + }, + "execution_count": 393, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_language.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 394, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Fraction of total queries in the year (%)')" + ] + }, + "execution_count": 394, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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PibS+bwICAuDm5mb0/efu7o6jR49adO30vp6DgoKg0WiMPl+G3xfW/llBREQ5G3/aE+UCLi4uKF26dJr7DZOKqKgodO3aFfny5UPz5s3Rvn17BAUFYe3atQBglKC8Dltb21Tb9H8o6z9a2raptgAgISEBCQkJGRZbWm2NGTMGf/75Jzp27IiPPvoIBQsWRI8ePcxex97ePtUkfYbvvT4ZMmSYaCckJGDAgAHo0qVLmm0YPjaUkJAABwcHk6sPFCxYEA4ODti9ezcuXryI06dPY/v27diyZQt++eUXFC5c2OxrS/m5S/k606NQpJ7uRghhNmZT2rdvj/3796NNmza4c+cOWrVqBSDxfWzXrh2GDh1q8tqLFy/G9u3b8eGHH6Jjx46YOnVqqvkr0vq6M8WSr6m0Plev057hx5QJWkJCgsmvKT3DYpz+eMPPheE103s9+uum/JynfG7Yhr+/Pz766CM0adIEtWrVQr9+/fDDDz+kGx9g+mtFL+XrT+9YQ6Z+7uhjVyqVZl9XWiz5OZHW942565r7WZHe17Opz5fhY2v/rCAiopyNkxQSvWOuXLmCp0+fYvPmzRg4cCC8vb0REhIi/wFZunRpBAQEGN0FGz9+PJYuXZpuu+XLl8ft27eNegT4+vrC2dlZvotrqVKlSkGlUuH69evytv/++w/BwcEoV67ca7Wlj82wLX1s+juFhn+MR0ZG4sCBA1i4cCG+/PJLtGjRAi9fvgRgPnEoX748oqKiEBQUJG/T39kDkpMFw8nA9JOGAUDZsmXx8OFDlC5dWv63b98+i+4qli1bFtHR0UhISJDPBYA5c+YgMjISp06dwvbt29GgQQNMmjQJhw8fRlRUFP744w+zbb+O9BLW143ZlLZt28LPzw+7du2Ct7c38ufPL7d1//59o/fu3Llz8gSb27Ztw6RJkzB69Gi0a9cOMTExAN581QVzX1Nv0p657587d+4YfV/eunVLnjzRFD8/P/lxcHAwIiIi0jw+vdeTN29eFCpUSL5DDgAPHz7Eq1ev0rz2vn37ULNmTSxatAgff/wxqlevjuDg4Gy3ykWFChWMXldkZKQ86Wd63ubnBABUrFgRAQEBRgUlwzhsbGwQFRUltyWEMPpZkd7Xc6lSpWBjY2PU3q1bt+TH2eVnBRERZU8sEBC9Y5ydnRETE4OjR4/i0aNH2LlzJ7Zs2SJ3z/Xx8UFUVBRmz56Ne/fu4cCBAzhw4AAaNGgAIHGIwj///IOIiAijdtu3bw+dTocpU6bg7t27OH78OJYtW4aePXtafKdPz8HBAT179sSsWbNw6dIlBAQEYMyYMShcuLAchyVt6OPs1asX7ty5g0WLFuHevXvYu3cvtm7dKg+dcHBwQEREBO7duwdbW1vY29vjyJEjePToEc6dO4dvv/0WANLswqxXqlQptGzZEhMmTIC/vz9OnDiBTZs2yfsrVqwIOzs7LF++HA8fPsTGjRuNCgj9+vXD4cOHsWnTJgQHB+Pnn3/G6tWrjbopp6V8+fJo0KABxowZg+vXr8Pf3x9jx45FaGgoChUqBJ1Oh3nz5uHw4cN49OgR9u/fj/j4eFSuXNmi99NShu+luWEI5mI2pVChQvD09MTGjRuNuqv36tULfn5+WLhwIe7fv4/Dhw9j/vz58h1PZ2dnnDx5Eg8fPsQff/yBMWPGADD/OU2Lua8pS+TJkwdBQUEIDw+36Pvn8ePHcpfy1atX49atW+jWrVua7f/00084duwY/P39MWHCBHzwwQcoX768yWP79euHo0ePYtOmTbh//z42bdqEo0eP4uOPPwaQOIP+8uXLcf78efj7+8srH6RVEHJ2dsadO3dw/fp13L9/H3PnzsXNmzdNDinKSn369MFPP/2E33//HXfv3sXEiRMRHR1tttD1Nj8nAKBdu3aIjo7G7NmzERQUhJ07d+LQoUPy/mrVqiEyMhLr1q3Dw4cPMW/ePLkAAaT/9ZwnTx506dIFc+bMwV9//YW//voLs2bNApD4+couPyuIiCh7YoGA6B3j4eGB4cOHY8aMGfDx8cEvv/yCqVOnIjw8HCEhIXBycsLatWtx48YN+Pj4YNmyZZg9e7Y8nrlfv35YuHAhli9fbtRunjx5sH79ejx8+BCdOnXCt99+i08++QRffvnlG8U5evRo1K9fH19++SV69uwJtVqNH374weJu4IZxFilSBGvWrMG5c+fQoUMHrFy5EmPHjpWTqw8++ADlypWDj48PAgICMH/+fBw7dgxt27bF7NmzMXjwYBQuXNgomU/LzJkzUbhwYfTo0QOLFi3Chx9+KO9zdHTEjBkzcPz4cbRv3x63bt3CJ598Iu93d3fHggULsGPHDrRr1w6bNm3C7NmzLZ5zYd68eShdujQ+/fRT9O7dG4UKFcLKlSsBJC5D99VXX2HevHlo3bo1Nm7ciIULF75Rj4z0GL6X/v7+bxVzWtq1awdJktCsWTN5W/HixbFmzRpcuHAB7du3x3fffYcvvvgCvXr1AgDMnj0bd+7cQbt27TB27Fi0bt0a7u7uFn1OTTH3NWWJjz/+GNu2bcPkyZMt+v6pWrUqIiIi0LlzZxw6dAhr165FmTJl0my/c+fOWLp0qdz9Pb1eQNWqVcOCBQuwfft2tG/fHr/88guWLFmCevXqAUhc3rFly5b48ssv0bdvXzRq1AgqlSrNYUN9+vRBzZo10b9/f/Ts2ROPHz/G8OHDjXo1ZAft2rXDgAEDMG3aNHTr1g1FihRBiRIlzA6HsrGxeaufE3ny5MHq1avxxx9/wMfHB7t370aHDh3kYSmlS5fG2LFj5aUqNRqNUUHM3Nfz2LFjUblyZfTv3x9ffPEFOnToIMcNZI+fFURElD1JIrv19yMiykV2796NJUuW4MyZM1kdSq6xfPlyBAYGYsmSJVkdSqZZtmwZLly4gJ9//tmi45s2bYohQ4a8VsEiPWfOnEHVqlXl4Q5hYWGoW7cujh8//lqTMWY3V65cQcmSJeUJMbVaLT744AOsWLECXl5eVrvuw4cP8ezZM9SuXVveNn36dMTExGDu3Llv3f6xY8dQt25d5MmTBwBw48YN9OrVC76+vm88zwwREb0b2IOAiIhyhICAAOzduxebN29ONeM+Wdf27dsxfvx4BAYG4u7du5g2bRqqVauWo4sDQGIiPWLECNy+fRvBwcGYM2cOHB0d5ZUerCUyMlIeUvT48WMcOXIE+/btS7UazZtavnw5Zs2aheDgYNy+fRvz589H06ZNWRwgIiKzWCAgIqIc4fbt25g2bRo6dOgAb2/vrA7nnTJlyhQolUr07NkT3bt3h06nw4oVK7I6rLc2YsQIlC1bFv3790fHjh0RFBSE9evXv9aKFm/Czc0NU6dOxaJFi9C6dWssXLgQ48ePf+0lXNOyYMECPH78GJ06dUL//v1RokQJeR4CIiKi9HCIARERERERERGxBwERERERERERAaqsDsCa0lsfmoiIiIiISC8gICCrQyDKcrm6QADwG52IiIz5+fnBzc0tq8MgIqJshDcWiRJxiAERERERERERsUBARERERERERCwQEBERERERERGsXCDw9/dHjx494O7ujg4dOuDGjRvpHv/w4UN4enri1atXJvdv2LABTZs2tUaoRERERERERO80qxUI4uPjMXToULRp0wZXr17F4MGDMWDAAERGRpo8/tixY+jVq1eaxQF/f38sXbrUWuESERERERERvdOsViC4cuUKNBoN+vXrBxsbG7Rr1w4VKlTAoUOHUh27a9cuzJs3D8OHDzfZVmxsLEaPHo2PP/7YWuESERERERERvdOstsxhYGAgypcvb7StXLlyuHPnTqpjGzdujM6dO+PJkycm25o3bx6aNm2KatWq4fDhw68Vh5+f32sdT0REuVtsbCx/NxARERGZYLUCQXR0NOzs7Iy22dvbIyYmJtWx7733XprtnD59GtevX8e2bdtw+vTp146Da10TEZEhPz8//m4gIiIiMsFqBQIHBwfExcUZbYuJiYGDg4PFbYSGhmL69OlYt24dbGxsMjpEIiIiIiIiIkpitTkIypcvj3v37hltCwoKQoUKFSxu49y5cwgNDUWPHj1Qu3ZtjBo1CiEhIahduzZCQkIyOmQiIiIiIiKid5bVCgReXl4QQmDTpk3QaDQ4ePAgAgIC0KJFC4vb6NixI65fv45r167h2rVrWLBgAYoVK4Zr166hWLFi1gqdiIiIiIjeEdfuh2V1CETZhtUKBGq1GuvWrcPvv/+OOnXqYPXq1VixYgVcXFzw66+/wsPDw1qXJiIiIiIiMmvqvlvo8/2VrA4j23B1dcXJkyezOgzKQlabgwAAKlWqhJ9//jnVdh8fH/j4+KTaXqJECQQEBKTZXvPmzdG8efMMjZGIiIiIiHIvIQR0AkjQCeiEkD/+cf8/7Lj2EDEaHWyzOkiibMKqBQIiIiIiIkqmT1YNE9XEj4BOJ5AgBHRJz/WP9cclHosUz5O36/THJ20XSUmxRW0KAZF0XvK+pGvp2xCAlNVv4BtSSBKUCkChkKCUJCgkCTcfv4RWJ7I6NKJshQUCIiJ6Z1y7H4YLgRGIdghDrdIuWR0OUbaQnPwJ6JISxcTkUhgklzBIMlMnkKkSSsNj0jhXTmgNklIhkq9vKnFOjivxuJyerEqSBKU+YVVIUEiAUiEl7U98rn+sVEiJx+sTXcngOLmNxATYRqFIsw3D5wrJ+Fx94qxQJMclJcWkjzE3uXY/DEdvP4MmISGrQ8kxzp49i2XLlsHf3x+SJKFGjRqYOnUqypcvj0ePHqFZs2ZYunQpli5diqdPn6Jq1aqYPn06ypUrBwDw9/fH9OnT8ffff6Ns2bLo1KkTNm/ejBMnTuDy5cv45JNP8OeffyJPnjwAgGXLluHkyZPYvXu32eubax8A7t27h5kzZ+LatWtwcXFBmzZt8NVXX0GtVmfBu5k9sUBARETvhKn7bmHHtUcQQodVV8LQvXYJTO9YNavDohRSJqU6IVLc8US6CaWcVOqTUoNzE++QGt9RTdmm4R1V48RZ30bKGJOT4jRfU9JHa6ZWb3MNSYJBgpqcfKZMIOWENmVSKsHgmNQJpUplok05YU3dpsIgnrQT58RtkpS7ElbKXLXLuKB77RLYce0RdFkdTA7w+PFjDBkyBF9//TUWLlyI0NBQTJ06FfPnz8fq1avl45YvX45vv/0Wtra2GDNmDObNm4fVq1cjIiICn376KRo1aoSZM2fi5s2bmD59OvLnz58h1zfXflxcHAYMGICGDRti4sSJCA0NxbfffovIyEh8++23VnnPciIWCIiIKNe7dj8MO649QoxGf5coATuuPYKPe7F0exKkNW411V1Wg6TUKKE1THJNJJSmktJUbaa4A6vv5muqi7Bh4pyTJd/pNH2nNHUCaXCHVJKS78qmPFeSoFJKsFUZJ7QKw7unBt2PFQqkbjPF9Qzv0jJZJcp5pnesCh/3Yui1I6sjyf4SEhIwduxY9OnTBwBQsmRJdOzYEVu3bjU6bsiQIfD09AQA9OrVC2vXrgUAHDp0CJIkYfr06VCr1ShfvjwCAwNx6NChDLm+ufYPHDgAGxsbTJ06FZIkoVy5cpg+fTo+/vhjjBkzBo6Ojm//JuUCLBAQEb0jDJNdfbKakJD4UavTJSe9CfrkVIcEHeR9Wp1OTkRTbtMmCON9Btu0SeNXtQYJreG2zBjTevvJK2gTjO8PaXU6rD4dhPeLvkj3XLlLrtEdUglK/R1NfZIod8tNnZQqDI41vMuqUkiwVSlStIHUd1kNu/mm0aZhkquPkYiIzOOQM8uUKlUKLVu2xNq1axEYGIigoCD4+/ujUKFCRseVKVNGfuzo6AitVgsACAgIQOXKlY2687u7u1tcIDB3fXPtBwYG4uHDh6hZs6a8XwgBnU6H+/fvo2pV9ioEWCAgolxAiHSSz3QSU6NEWX+Mfp9RomxwfoptCWm0k9F3cN+2i7L+fGXSHVSFJEGVlFyqDJLMlNtUiuRjlSm22dpIsJeUyftStK1M+U9KPj/lNmu7dj8M5/55AY0ueZypSqHA4Ebl+IchERGRBe7cuYMePXqgXr168PT0RLdu3XD9+vVUPQhsbGyMnguR+FeISqWCTpf2YA5ThW19ccGS65trX6vVwt3dHXPmzEm1r3Dhwmme965hgYAomzJ5t9dUQppWwmriDrDhNqN9ctdmw336u7vJSbW1JoV62+TXcPzs6ySmpo5TKiSoVYo095narlIokrsip7jLTNmD4ThTIXSQJAV61C7J4gAREZGF9uzZg/fffx/Lly+Xtx08eFAuAJhTsWJFHDx4EPHx8fJd/ps3b8r79YWFyMhIeZLCR48eWXx9c+2XL18ehw4dQpEiRWBrm7iw5Y0bN7B+/XrMmjULdnZ2lr8ZuViuLxD8EcyZqrMb/eROqZPP5MQ2ZddkU9uM7gSL1NsM7/am3KYTKe4gW3Fyqbc53/COrFIBKBWKxG7NSoXR3d7kfcmJa8o7wGpVin1GbZu+Y2yYVBtuI8qJ9ONMz98IRL3qFfi7gYiIyIS///4bSqXSaJurqyvy58+PoKAgXLt2DYULF8bx48exc+dOODs7W9Ru+/btsWTJEkybNg0DBgxAQEAANm/eLJ9fsWJF2NnZYcmSJRg6dCguX76MU6dOoXTp0gBg9vrm2vfx8cGKFSswduxYDB06FFFRUZg0aRKKFi0KJyenjHjrcoVcXyDovf5Kps1UrUuR9Bomn2l2bU7j+JR3gE1v08l3dBMSdEgQKe8Am7/bm1XJr8nk00S355SJqdE+g7u9ihRJr2GibGqbqTvDvNtLlPvVKu0Ch2gnuLE4QEREZNKyZctSbZszZw769OmDgIAADB48GJIk4f3338e0adMwefJkPH361Gy79vb2WLNmDaZNm4aOHTuiQoUK6Nq1K06fPg0gcb6C2bNnY9GiRTh48CC8vb0xfPhw7N+/HwDMXr9IkSLptu/g4IANGzZgzpw56NatG+zs7NCkSROMHz8+A9+9nE8SlvYJyYFcXV0R13kRVAoJnT2Ko5iz/Wud/7rJr9FdXsO7vQrJeJvR3V5T28x3bTYcM2xyH+/2EhGZ5OfnBzc3t6wOg4iIshFXV1cEBARkdRi52sOHD/H48WN88MEH8rb169fjzJkz+PHHH7N9+++KXN+DAADUKgW8yhVA11olsjoUIiIiIiKid05UVBQGDBiAOXPmoFatWrh37x42bdqEL774Ike0/654JwoEQgBl33PI6jCIiIiIiIjeSZUrV8a3336LFStWICQkBAULFkS/fv3QvXv3HNH+uyLXDzFQdF+KHrVLYlrHKlkdDhERZQMcYkBERClxiAFRolzfg+CngXU4UzURERERERGRGYqsDsDaWBwgIiIiIiIiMi/XFwiIiIiIiIiIyDwWCIiIiIiIiIiIBQIiIiIiIiIiYoGAiIiIiIiIcpCHDx9mdQi5FgsEREREREREucy4ceMwa9Ysq7X/4sULjBkzBnXr1oW7uztatmyJ5cuXQ6vVvnZbISEh8PDwQEREhNljt2zZgrlz575JyGSBXL/MIRERERERUVa6dj8M90OjUfY9h1yzytrXX3+NUqVK4ffff0fevHnxzz//4IsvvoBGo8HIkSNfq61ixYrB19fXomPDwsIghHiTkMkC7EFARERERERkJVP33UKf769gyr5b6L3+Cqbuu5Wp14+Pj8fMmTPRsmVLuLu7o0WLFjh48CAA4JtvvjHqZZCQkABvb29cvnw53fMA4Pr162jVqhXy5s0LAKhYsSImTJgAe3t7+ZiTJ0/Cx8cHHh4e6NSpE65evQogsXfDV199haZNm6JVq1a4f/8+XF1d8erVKzx69AjVqlXDunXr4OXlBW9vb6xduxYA8Pvvv2PNmjU4deoUfHx8rP7evYtYICAiIiIiIrKCa/fDsOPaI8RoEhAdn4AYTQJ2XHuEP4LDMi2GDRs24NatW9i5cyf+/PNPfPLJJ5gyZQq0Wi06d+6M3377DQkJCQCA8+fPw87ODnXq1En3PABo06YNRo0ahVmzZuHYsWMICwtDw4YNMXjwYADAP//8gxEjRmDEiBH4448/0K9fPwwdOhQxMTEAgEuXLmHLli3YtWsXVCrjju3x8fH466+/cPz4cWzcuBGbNm3CwYMH0apVK3z++edo3Lgxfv3110x7D98lHGJARERERET0Gn7/+yluh7wye9ztJ6+gTdAZbdPqdFh9OgjvF31h9vz3i+VFqypF3jhOAOjZsye6d++OvHnz4tmzZ7C3t0dkZCRiYmLg7e0NhUKBy5cvw9vbG/v370eHDh0gSVK65zk5OWH27NnYt28fDh8+jN27dyMqKgp16tTB5MmTUbFiRfz222+oW7cumjdvDgDo1KkTSpcuDaVSCQCoU6cOihYtCgB4+fJlqrgnTJgAR0dHuLq6olu3bjhw4ADatWv3Vu8FmccCARERERER0WtoVaWIRYn7tfthOPfPC2h0CfI2lUKBwY3KZdpcBJGRkfj2229x/fp1FC9eHGXLlgUACCGgUCjg4+OD/fv3w8PDA8eOHcPu3bvNngcACoUCnTt3RufOnZGQkICbN29i1apVGDhwIE6cOIEXL17IBQA9Dw8P+XGhQoXSjFmlUqF48eLy8yJFiuDChQsZ84ZQujjEgIiIiIiIyApql3FB99olYG+jhINaCXsbJXrULmmV4sDevXuxZcsW+blGo4GdnR2mTp2KEiVK4Pz589i9ezcGDBhgdF7nzp1x7NgxHDlyBJUqVZILAemdd+bMGdSvXx8ajQYAoFQq4e7ujunTp+Pp06d4+fIlChcujGfPnhld63//+x8eP34MAJAkKc3XotVqERoaKj8PCQlJVWwg62CBgIiIiIiIyEqmd6yKnwbWwbdJH6d1rGKV60RFRWHLli149eoVQkJCcPXqVVSuXBkRERGwtbWFQqHA8+fPsXDhQgCQk/vy5cujdOnSWLJkCTp27Ci3l955tWvXhkqlwoQJExASEgIAeP78OVatWoWaNWvCxcUFbdq0wcWLF3H69GnodDr8+uuv2LJlC5ydnS16PQsWLEBcXBz8/Pywc+dOdOrUCQCgVqstWg6R3gwLBERERERERFZUq7QLutYqYdVhBd26dUPVqlXRokULdOrUCS1btkSbNm0wceJEnDt3DrVq1ULPnj3h6emJ/Pnz486dO/K5nTt3xr///ou2bdvK29I7z8HBAVu2bIEkSejevTtq1KiBLl26QKfTYcWKFQCAcuXKYenSpVi4cCFq166NH374AatXr0aePHksej3Ozs5o2rQphgwZgi+//BJNmzYFADRu3BjBwcFo1KhRBr57pCeJXLyIpKurKwICArI6DCIiykb8/Pzg5uaW1WEQEVE2wrwh+3j06BGaNWuGq1evyksoUuZhDwIiIiIiIiIiYoGAiIiIiIiIiLjMIREREREREWUTJUqU4HCPLMQeBERE9O54cAn57h0EHlzO6kiIiIiIsh32ICAionfDodGA708orBOA70LAozfQdn5WR0VERFntwaWsjoAo22APAiIiyv0eXAJ8fwI00VAmxACa6MTn7ElARPRuOzQa2Nw5q6MgyjZyfw+CB5eBUl5ZHQUR5RZCAEJn5l92OsYK14GU1Z+FdBiu3GsQ59ObQILG+NAEDXB+KXC3WqZERkRE2czLR8CN7YBOA4DL6REB70KBYHMndiOljCNEzk7sXucYpNyWUxJEK5MkQFIYfEzvX0YcY2K/QmWF67zGMTnRg0tA0MmkPwKTKG2Ael+yiExE9K76ayvw9x7j3w1E77jcXyDQdyOt2jVj/gi0OKnLBQlimsnhO+5tEruMOCZVcpiRseTiBJHebaU+SCwW+/6EBJ2AUiEBHn1YHCAiepe5lAOMep4RUe4vEAAZ2400vbuHSLktJyaIFiaRREQ5Tdv5QNWueHbrLIpVbcDiABHRu86geGx1Dy4BYUGAS3mr/f5xdXXF3r174ebmZrS9Y8eO6Nu3L0qVKoVBgwYBAIQQiImJgYODg3zcunXrcPHiRSxfvhyffvopxo4da9TOr7/+itGjR2P48OH44osvsGzZMvj5+WHlypVpxjRixAj07NkT3t7eeP78OZYvX47Tp0/j1atXKFSoED788EMMHDgQCoUCABAcHIx58+bhypUr0Gq1KF68OHr27InevXvLbd68eROLFi3CjRs3IIRA2bJlMWDAALRt2/at30N6VwoE7EZKREQAUMoLL6PyolgpN/PHEhFR7pdUPMaPn1jvGkmr6AASAJFlw59r164NX19fAMCjR4/QrFkznD59GnnzJs+/cPHiRTg7O+PgwYMYPXq0nLgDiQWCPHnyWHy9+Ph4XL9+HQsXLsTz58/RpUsX+Pj4YM+ePXBxcYG/vz++/vprhISEYNq0adDpdBg0aBDat2+PBQsWwM7ODn/99ReGDRsGtVqN7t27IyIiAp9++ilGjx6NtWvXQqFQ4OzZs/jqq6+QL18+1KtXL+PesHeUwvwhOZyNA7uREhERERGRadbMEwxW0YEmKkesouPh4QEAuHr1qrztxYsX8Pf3h6enp8XtXL58GR4eHrCxscH//vc/VK9eHWPGjIGLiwsAoHLlypg7dy4iIyOh0Wjw33//ITg4GO3bt4e9vT0kSYKHhwfGjBkjt3nv3j1ER0ejXbt2sLGxgVKpROPGjfHVV18hOjo6g96Bd1vu70HQZy+LA0RERERElHH8DiSukGPO266iU6Qa4Nbe4rB69eoFpVJptC0qKsri8wFAoVCgXbt2OHjwILy8EvOoAwcOoHXr1ggJCbG4nRMnTqBp06YAgDNnzuCbb75JdUz16tWxYMECAECBAgVQp04d9O/fHz4+PvD09ISHhwc6deokH1+5cmWULFkSH374Idq3b4/atWujRo0a6Nev32u9Rkpb7i8QsDhAREREREQZya29ZYl7Jq+is3XrVpNzELwuHx8f9OvXD5MnT4aNjQ1+/fVXTJ8+HatWrbK4DX3XfwAICwvDe++9Z/ac77//Hjt27MDRo0fx448/QqvVolGjRpgyZQqKFCkCtVqNnTt3YuvWrTh58iRWr14NhUKBNm3aYOLEiUbDJejN5P4hBkRERERERFlBPxGijQOgzpNjhj+7ubmhYMGCOHv2LO7evYvY2FhUq2b5hO9+fn4oWrQo8uXLBwAoVKgQ/v33X5PHhoaGyo/VajV69+6NH374AVevXsX69esRGhqKL7/8Uj7GyckJn3/+ObZt24arV69i8eLFuH79OqZNm/ZmL5aM5P4eBERERERERFlFPxFi2F2rrmKQ0Tp06IADBw6gZMmSr90L4fjx4/LwAgBo1KgRjh49ajRcAAB8fX3Rq1cvHDt2DOfOncO2bduwZ88eAInFgrp168LGxgYDBw4EACxevBiBgYFYsWIFAMDe3h7NmjVDZGQk1q5d+xavlvTYg4CIiIiIiMiaSnkB7r1yTHEASCwQnDx5Evv374ePj4/JY+Li4vD06VOjf7GxsTh58qRRgWDw4MH4448/sGDBAoSFhUGn0+GPP/7A6NGj0a1bNxQvXhyNGjXCgwcP8N133yE0NBRCCDx8+BAbN25Es2bNAAAtWrTA2bNn8f333yMiIgI6nQ53797Fzz//jObNm2fK+5LbsQcBERERERERGSlWrBjef/99KJVKFC1a1OQx586dQ6NGjYy2LViwALGxsShdurS8rXDhwti+fTuWLFmCDh06IDo6GoULF0aPHj3w6aefAgCKFCmCrVu3YtmyZWjfvj1iYmKQP39+tG7dGiNGjAAAVK1aFRs2bMCqVauwZs0axMfHo3DhwujcuTMGDRpkpXfi3SIJIUR6B4SFheH06dO4d+8eFAoFypUrh6ZNm8LR0TGzYnxjrq6uCAgIyOowiIgoG/Hz80s1gRMREb3bmDcQJUpziEF8fDy+++47+Pj44MiRI4iJiUFcXByOHTuGdu3aYf78+YiNjc3MWImIiIiIiIjIStIcYjBs2DC0b98eI0eOhFqtNtqn0Whw4MABDBs2DN9//73VgyQiIiIiIiIi60qzQLBkyRLkyZPH5D4bGxt07twZrVq1slpgRERERERERJR50hxiYKo48PDhQ/zzzz/ycwcHB+tERURERERERESZyuJlDjdt2oSpU6di7ty5mDZtmkXn+Pv7o0ePHnB3d0eHDh1w48aNdI9/+PAhPD098erVK3lbTEwMpkyZgvr166NOnToYMmQIQkJCLA2biIiIiIiIiCyQZoEgKCjI6PnFixexYcMGfP/997h48aLZhuPj4zF06FC0adMGV69exeDBgzFgwABERkaaPP7YsWPo1auXUXEAABYuXIgHDx5g//79OHPmDN577z18/fXXlrw2IiIiIiIiIrJQmgWCuXPnYt68eYiOjgYAlC5dGpMmTcLUqVNRsmRJsw1fuXIFGo0G/fr1g42NDdq1a4cKFSrg0KFDqY7dtWsX5s2bh+HDh6faFxcXh+HDhyN//vyws7PDxx9/jOvXr0Or1b7O6yQiIiIiIiKidKQ5SeHatWvx22+/oV+/fujduzcmTJiA8+fPIz4+HvXq1TPbcGBgIMqXL2+0rVy5crhz506qYxs3bozOnTvjyZMnqfbNmDHD6PmxY8dQsWJFqFRphk5EREREREREryndLLtNmzZo0qQJVq1ahf79+2Ps2LGoXLmyRQ1HR0fDzs7OaJu9vT1iYmJSHfvee+9Z1ObBgwfx/fffY+3atRYdDwB+fn4WH0tERLlfbGwsfzcQERERmZBmgeDx48fYsmULbGxs0LdvX0RGRmLu3LkoUqQIvvrqK+TNmzfdhh0cHBAXF2e0LSYm5o1WPhBCYMWKFfjhhx+wYsUKeHp6Wnyum5vba1+PiIhyLz8/P/5uICKiTOX73BcPXj1A6byl4V7I3WrXefHiBebNm4ezZ88iJiYGhQoVgo+PDwYPHiz3wD516hQ2bNgAPz8/SJIEV1dXDBgwAI0bN5bbadq0KSZMmIDmzZtbLVbKntKcg+DLL79E1apVUaxYMYwZMwalSpXCypUr0aBBAwwaNMhsw+XLl8e9e/eMtgUFBaFChQqvFaBGo8HXX3+NvXv3YuvWrfD29n6t84mIiIiIiLLK7Muz8fnRzzHr8ix8dvQzzL4822rX+vrrr6FWq/H777/jr7/+wooVK3DgwAEsW7YMALB582ZMmjQJPXv2xKlTp3Du3Dl89NFHGD9+PDZv3my1uCjnSLNA8PLlS7Ro0QKtW7fG8+fP5e1NmjSx6IvHy8sLQghs2rQJGo0GBw8eREBAAFq0aPFaAc6ePRv+/v7YsWMHKlas+FrnEhERERERZRXf577YG7gXMdoY+d/ewL346/lfVrne9evX0apVK7m3d8WKFTFhwgTY29sjLCwM8+bNw/z589G2bVvkyZMHarUabdu2xaJFi/Ddd98hNDTUKnFRzpHmEIMBAwagdevWsLGxwTfffGO0T61Wm21YrVZj3bp1mDp1KpYuXYoSJUpgxYoVcHFxwa+//oqpU6fC19c33TZevXqFbdu2QalUolmzZkb7zpw5AycnJ7NxEBERERERZaTjD44jICzA7HH+Yf7Q6DRG27Q6LTbc2oDKLubndnN1cUWzUs3MHqfXpk0bjBo1Cj4+PvDy8kLNmjXRsGFDNGzYELt374azszPq1q2b6ry6deuiUKFCOH36NLp06WLx9Sj3SbNA0LNnT/Ts2fOtGq9UqRJ+/vnnVNt9fHzg4+OTanuJEiUQEJD8jZY3b15OJEVERERERNlKs1LNLErcfZ/74tKTS9DqkpdoVylU+LTqp1aZi2D27NnYt28fDh8+jN27dyMqKgp16tTB5MmT8e+//6JIkSJpnluoUCH8+++/GR4T5SxpDjFYsmQJ4uPj0zwxLi4OixcvtkpQREREREREOZ1HIQ90qtAJ9ip7OKgcYK+yR+cKna02UaFCoUDnzp2xZs0aXLlyBdu2bYO9vT0GDhwIFxcXk8vK6/37779wcXGxSlyUc6RZIKhZsyY+/PBDzJs3D1evXsWzZ8/w9OlTXLlyBQsWLMCHH36IGjVqZGasREREREREOcoErwlY22Kt/HG813irXOfMmTOoX78+NJrEIQ1KpRLu7u6YPn06nj59iiZNmiA8PBznzp2Tz9m9ezeCg4Nx7do1PHv2DA0bNrRKbJRzpDnEoGHDhqhVqxa2bNmCuXPnIigoCEqlEmXKlEGrVq2wbds2ODo6ZmasREREREREOY57IXerLm8IALVr14ZKpcKECRMwcuRIFCtWDM+fP8eqVatQs2ZNvPfeexg3bhzGjh2LiRMnolGjRggODsasWbOgUqnw5ZdfonDhwnJ7//33H54+fSo/lyTJaD/lTpIQQmR1ENbi6upqNKcBERGRn58f3NzcsjoMIiLKRnJL3vD48WMsXboUFy5cQEREBJycnNCkSROMHDlSHj5w+vRprF+/Hv7+/hBCoGLFiihatCgCAwMxbtw4eHt7o2nTpnj8+LFR22q1Gjdv3syKl0WZiAUCIiJ6p7BAQEREKTFvAG7evIn4+HjUqlUrq0OhLJTmEAMiIiIiIiJ6N1SrVi2rQ6BsIM1JComIiIiIiIjo3WG2QNC3b9/MiIOIiIiIiIiIspDZAkFERASio6MzIxYiIiIiIiIiyiJm5yCwt7dHkyZN4OrqCgcHB3n76tWrrRoYEREREREREWUeswWCrl27ZkYcRERERERERJSFzBYIOnfubPRcCIHg4GCrBUREREREREREmc9sgWDbtm2YN28eYmJi5G0uLi44f/68VQMjIiIiIiIiosxjdpLCtWvXYuPGjWjUqBH27NmDESNGoHnz5pkRGxEREREREWWChw8fZnUIlA2YLRA4OzujRo0acHNzQ2hoKIYMGYKrV69mRmxEREREREQ5XvSffyJ8z15E/+lrtWu4urqiRo0a8PDwgIeHB2rWrIkBAwbgzp07Zs/dsmUL5s6da9SWn5+f1WKl7MvsEAOVSoWXL1+idOnSuHHjBurVq4eEhITMiI2IiIiIiChHezpjJsJ37wYkCRACzl26oMjkSVa51rZt2+Dm5gYA0Gg0WLx4MQYNGoQTJ05AqVSmeV5YWBiEEFaJiXIWsz0Iunfvjs8//xyNGzfG9u3b0aVLF5QvXz4zYiMiIiIiIrIq3+fWu6sf/eefCN+9GyImBiI6GiImBuG7d1u1J4GejY0NOnfujKdPn+Lly5do1aoVdu7cKe8PCQlBtWrV8Pvvv2PNmjU4deoUfHx85P2HDx9GmzZt4OHhgZEjR8pz0kVFReHbb79F/fr14e3tjdGjRyMsLAwAsHv3bvTv3x/jx49HrVq10Lx5c2zbts3qr5UyjkXLHLZt2xYODg7Yvn07bt68ifr162dGbERERERERFYz+/Js7A3c+9rnRRw7hlg/f7PHxfr7Q2i1RtuEVovQ779H1PnKZs+3c6sMpzec/+3ly5fYvHkzKlasCBcXF/j4+ODQoUPo1q0bAODAgQNo0KABWrVqhTt37sDPzw8rV66Uz//zzz+xY8cOxMTEoGvXrti9ezc+/vhjTJkyBc+fP8fevXthZ2eHCRMmYPTo0fj+++8BABcuXMD06dMxY8YM7NmzB99++y3atm2LvHnzvtHroMxltkCg0+nw888/486dO5g8eTLu3r2LJk2aZEZsREREREREABKXW9fqtNDoNEgQCdDqtPJHw3/yNmGwTZdg/Fwk4G74Xey6swsanea1Y3Fq3tyixD36zz8RdeEChCb5GpJKhQIDBsChpsdrX9ecXr16yUMJ1Go1qlevjmXLlgEAfHx8sHLlSoSGhqJAgQI4ePAghgwZkmZbQ4cOhZOTE5ycnODp6YlHjx4hLi4Ov//+O7Zs2YL33nsPADB58mTUr18fz549AwAULFgQPXv2BAB06tQJkyZNwpMnT1ggyCHMFgjmzZuHsLAw3Lx5E0IInD17Fv/++y8mTbLOuBkiIiIiInp9Qgg5CU7QmUiSDZNpS49L8TxBJCQm6LoEJAjT85IJJI5llyBl6OuTIEGpUEKlUEElqaBUKGGjsIFSStym32cj2ciP9ftsbWxTHfck8gmUkhIavH6BwFIONWvCuUsX4zkIPuxileIAAGzdulWegyClkiVLonr16jh8+DA++OADhISEoGnTpmm25ezsLD+2sbGBVqvFy5cvodFoUKxYMXlfwYIFoVar8eTJEwBAgQIFjM4DEm86U85gtkBw8eJF7NmzB126dIGTkxM2bNiAjh07ZkZsREREREQZRgiRZrJrmDCbukMtH5dym/64FHeo9cdl+GtIJ/nWJ9D6RFifSBsmxfJzw2MUKtgr7dM+zyAZNzxXKSkhSRlbBMhMLwu/zJT4i0yehLzt2iE+OBjq0qWtVhywhI+PD3777TeEh4ejVatWUKvVr3X+e++9B7VajcePH6NgwYIAgGfPniE+Ph4FChRAUFCQNcKmTGTRKgYKRfJchmq1GiqV2dOIiIiIKIfRJ9CWdNt+7TvUhtsMkmp9wmuV12MimVZKyjSTXcOEWZ8U6x/bKe2gsklOmA2Plc81kYzn5AQ6t/Mo5IFOFTphb+BexCPeqtdyqOmRpYUBvbZt22LevHkIDQ3FtGnT5O1qtRoRERFmz1coFPDx8cHChQuxZMkS2NraYtasWfDw8EDJkiVx9epVK0ZPmcFspl+pUiVs2bIFCQkJCAoKwqZNm1C5svkJNYiIiIhyI33XapN3kHVaaETqO81v06XbkgQ6I7t0KyRFuneeUyXTSdvVSnX6x5k4T6lQQiGZXVSLyGomeE1A27Jt0ePbHlkdSqbIly8f6tWrh9u3b6N27dry9saNG2PLli1o1KgRTp8+nW4b48ePx4IFC9CxY0fExsaiQYMGWL58ubVDp0wiCTMLXkZGRmL27Nk4deoUdDod6tevj4kTJyJ//vyZFeMbc3V1RUBAQFaHQURE2YTvc19c8r+EupXrwr2Qe1aHk2vphM4oKTZ5BzmN7tiG3b4Nj9N3+zZ1nE7oXisxfttkWpIko3HP+sTXRmFjdHc51fjopI+Gx8ljplOOozZIsplAE1nfu5Q3zJw5E3ny5MHIkSOzOhTKhsz2IHj8+DFmz56dGbEQERFZjX4pK51Oh40PN6JThU6Y4DUhS2LRz8Sd3h3k15mJ29wd6gSRkOGThaX52iCggCLNO8imumMbJsW2NrZp3qGW72SnGEetVCgz5bUREeVkz549Q3BwMA4cOIAdO3ZkdTiUTZktEIwYMQL58+dHr1690Lp169eeyCKr/fX8L94lIiIyQQgBndBBQCQ+hi7NbfrHOpE4C7FOpH4sn2PicXptCiHkY16nTaOY02lTCIHgV8HGS1npgF13dkGr06KwQ2GrTCSWHsOZuI3uKlswE7faRm151++kfQpJwXHQRETvuN9++w1Lly7F8OHDUapUqawOh7Ips0MMgMSVDHbs2IFr166hQ4cO6NmzZ474onJ1dUW+Kfmy9C4R0ZswSm7MJV3mjtUndQLyY7NJl6mkzkybwBskihmZiOL1uhhTUvdqCVBAISeQEqTEx5AgSaYfW3RsUptG7afTZqr2LWwTSBwvba7NY8HHsMx3GWITYuXXb6+yxxjPMehUoVOOn4mbiIjezrs0xIAoPRYtR1C3bl3UrVsXFy9exKRJk/Djjz/C29sbkydPRsmSJa0d41uJ0cZgb+BetC3bNsN6EugTEx1SJF3pJGqp9qfxWG4z6fEbJ4ppnJcq5tdJFC1NRJE6kaPXZ5iAGSVCSJGcpZOopdqfxmMFFK+VKNpINubbz6hEMY1YTMVMlJZqBauZ/Bqp4FwBKgVX5iEiIiICLCgQREZGYv/+/di5cydiYmLQt29fdO7cGWfOnMHQoUOxf//+zIjzrWh1Wmy4tQGVXTJm9QV9kmIy0UpxR0t+rE/qkhKm9JKqdBO5FG1CMm5fAQVsFDav1abJRDEjE9GkY4mIsorhUlZCJyApJHSu0JlD0IiIiIgMmC0QNGrUCHXr1sWoUaPg7e0tb2/Xrh22bdtm1eAyikqhwqdVP+UfgkRE7zD9UlYX/S9yFQMiIiIiE8wWCA4cOICiRYua3Ld58+YMDyij2avseZeIiIgAAO6F3GEbagu3Qm5ZHQoRERFRtmO2QJBWcSCnWNtiLYsDRERERERERGYosjoAa2NxgIiIiIiIiMi8XF8gICIiIiIiykpPAsPhf/EJntx9mWnXHDduHGbNmpVp16PcwaK1nQ4fPgw/Pz8MHjwYx48fR/v27a0dFxERERERUY53ZlsA/C48gSQBQgBu3kXRsKdrVodFZJLZHgRr167Fzz//jMOHDyM2NhbLly/HihUrMiM2IiIiIiKiHOtJYDj8LjyBNl4HTZwO2ngd/C5kbk+C+Ph4zJw5Ey1btoS7uztatGiBgwcPAgC++eYbo14GCQkJ8Pb2xuXLl9M9j3Ivsz0IDh48iJ07d6J79+7Inz8/duzYgR49emDYsGGZER8REREREVG2EvTXv3jxMMLscS8eRUKXIIy26RIEfI8E42EJR7Pnv1fSCeXcC75xnACwYcMG3Lp1Czt37oSTkxO2bNmCKVOmoFWrVujcuTPGjRuHcePGQalU4vz587Czs0OdOnWwZs2aNM9TqSzqiE45kNnPrEqlglqtlp/nzZuXXxBERERERPTOKude0KLE/UlgOB76hRkVCRRKCR4tS6No+XzWDFHWs2dPdO/eHXnz5sWzZ89gb2+PyMhIxMTEwNvbGwqFApcvX4a3tzf279+PDh06QJKkdM9zcnLKlNgp81m0zOGpU6cgSRLi4+Px/fffo3jx4pkRGxERERERUY5VtIIz3LyLppiDoFimFQcAIDIyEt9++y2uX7+O4sWLo2zZsgAAIQQUCgV8fHywf/9+eHh44NixY9i9e7fZ8yj3MlsgmDx5MsaMGYOAgAC4u7ujRo0aWLBgQWbERkRERERElKM17OmKip5F8PJ5NPIVcrBacWDv3r2IiorCxx9/DADQaDSws7PD1KlTUbp0aaxcuRIqlQq3b9/GgQMH5PM6d+6Mnj174oMPPkClSpXkQoC58yh3MlsgKFy4MH744QfExMQgISEBjo7mx8oQERERERFRoqLl81m910BUVBS2bNmCDh06IDIyElevXsXYsWNx+fJl2NraQqFQ4Pnz51i4cCGAxAICAJQvXx6lS5fGkiVLMGjQILm9iIiIdM+j3MlsgeDFixfYuXMnQkNDjbZPmjTJakERERERERGR5bp164br16+jRYsWEELAx8cHbdq0QYkSJTBp0iRs27YN+fPnR/fu3fH333/jzp07qFu3LoDEXgRz5sxB27Zt5fYmTpxo9jzKfSRhZhBJnz59kDdvXlSuXBmSJMnbhw8fbvXg3parqysCAgKyOgwiIspG/Pz84ObmltVhEBFRNsK8gSiRRT0INm/enBmxEBEREREREVEWUZg7oGDBgggPD8+EUIiIiIiIiIgoq6TZg2DmzJkAAKVSiW7dusHb2xs2Njbyfs5BQERERERERJR7pFkgcHZ2BgDUqlULtWrVyqx4iIiIiIiIiCgLpFkg0E9CuHXrVvTq1cto39q1a60bFRERERERERFlqjQLBD///DNiY2OxadMmxMXFyds1Gg22bduGzz77LFMCJCIiIiIiIiLrS7NAoFKpcOfOHcTGxuLOnTvydqVSiXHjxmVKcERERERERESUOdIsEHTr1g3dunXDsWPH0Lx588yMiYiIiIiIiIgymdllDlkcICIiIiIiyt5cXV1Ro0YNeHh4wN3dHQ0bNsTixYshhHirdi9fvozatWune8w///yDjz76CAAQHByMYcOGwdPTEx4eHmjfvj1++ukn+djdu3ejY8eObxTLuHHjMGvWrFSPAUCn06FJkyaIiopC06ZNUb16dXh4eMj/PvroI1y9evWNrptR9u/fj44dO8LDwwOenp4YNGgQ/v77b3l/nz59sGnTpjdq29XVFX5+fqkev640exAQERERERHR23vsfxvhz57AuUgxFHd1s9p1tm3bBje3xPbv3buHfv36oUSJEujWrZvVrgkAJ06cQJMmTaDT6TBo0CC0b98eCxYsgJ2dHf766y8MGzYMarUa3bt3t1oM169fR/ny5ZEnTx4AwKJFi+Sb3TqdDlu2bMFnn32GEydOIH/+/FaLIy1XrlzBzJkzsXLlSnh4eCA+Ph4bN25E3759ceTIEbi4uGR6TKaY7UHwNvz9/dGjRw+4u7ujQ4cOuHHjRrrHP3z4EJ6ennj16pW8TQiBxYsXo27duqhduzZmz54NrVZrzbCJiIiIiIgyxPENq/HL7Mk4/v0q/DJrEo5vWJ0p1y1btiw++OADozvUW7duRYcOHVCrVi3UrVsX8+fPl/f98ssvaN68OTw9PfHhhx/izJkz8j6dToclS5agYcOG8PLywooVK4yudeLECTRr1gz//fcfgoOD0b59e9jb20OSJHh4eGDMmDFGx8fFxWHq1Knw9vZG/fr1sXPnTnnf06dPMWzYMHh5eaF58+YW31E/ceIEmjZtanKfQqHAhx9+iOjoaDx69AgAcPDgQbRv3x61atVC165dcfnyZQDAsGHDsHz5cvncNm3aYOLEifLzTz/9VI53x44daNWqFTw9PTFgwAA8fPgQAPDo0SN4eHhg0qRJqF27NrZt24br16+jTJkyqFWrFhQKBezs7DBkyBC0a9cOYWFhcvt37txBz5494eHhge7du+P+/fvyvuPHj8PHxwe1a9dGz549cfv2bYvem9dhUYHg8ePHuH37Nv7++2/5nznx8fEYOnQo2rRpg6tXr2Lw4MEYMGAAIiMjTR5/7Ngx9OrVy6g4AADbt2/H0aNHsWfPHhw5cgQ3b97E6tWZ801FRERERET0ph7738bfp45CExcHTVwsNHFx+PvUUTwOeLPu368jMDAQV65ckZPmP//8E0uWLMGSJUvwxx9/YM2aNfjhhx9w48YNhIWFYfLkyVi1ahWuXr2Knj17YubMmfLwhKioKMTFxeHEiRNYtmwZli1bhsDAQABAaGgowsPDUb58eRQoUAB16tRB//79sXDhQpw5cwYRERHo1KmTUe+Be/fuoXz58jh//jxGjRqFadOmISIiAgkJCRg8eDCKFi2KM2fOYP369fj555+xd+9es6/35MmTaRYIoqKisGHDBhQoUAAVKlTAuXPnMGnSJEyaNAmXL19G//798fnnn+PBgwdo3LgxLly4AAB49uwZQkJC5OJBdHQ0/vjjDzRu3BhHjhzB//73PyxatAjnz59HnTp1MGjQIPlmdnR0NFxcXHDhwgX4+PigcePGuHPnDj755BP8+OOP+Pvvv5GQkIDp06ejQoUKcqxnzpzBrFmzcOnSJTg7O2Px4sUAgJs3b2LUqFEYP348Ll26hI8++giffvppqvz5bZkdYrB06VL5zdSTJAnHjx9P97wrV65Ao9GgX79+AIB27drhp59+wqFDh1J1Ldm1axfWrl2L4cOHY8qUKUb79u7di759+6JIkSIAgC+++AJjx47F8OHDLXqBREREREREGemfqxfx7/0gs8c9vx+EhBS9nxO0Cbj66y4Elyln9vyCZcqhomddi+Pq1asXlEoltFotYmJiUKtWLVSrVg0A4Obmhr1796JYsWL477//EBsbizx58uD58+coV64cVCoVdu3ahfbt26NLly7o2rUrJEkCkLjC3ciRI6FSqVCnTh289957ePToESpUqICTJ0+iSZMmcgzff/89duzYgaNHj+LHH3+EVqtFo0aNMGXKFDmnK1q0KD755BMAQNu2bTF27Fg8ffoU0dHRePDgAXbt2gWVSoUyZcqgf//+2LZtGzp16pTm63748CHUarXcPgCMGjUKKlViuqtUKlG5cmWsXr0a9vb22LdvH3x8fPDBBx8ASMxVd+/ejYMHD6Jr166YPn06IiMjceHCBbRs2RLHjx9HSEgI/Pz84OrqioIFC2LHjh345JNPUKVKFQDAZ599hs2bN+Py5csoXbo0AKBDhw5Qq9VQq9WoWLEi9u7diy1btmD79u2YNWsW8ufPj759+2Lw4MHye92tWzeUL18eANCsWTPs2LEDQGLO7OPjg7p1E78eOnbsiK1bt+Lw4cMZOnTDbIFg3759OHLkCAoXLvxaDQcGBsovTK9cuXJGSybqNW7cGJ07d8aTJ0/MtlOuXDk8f/4c4eHhcHZ2fq2YiIiIiIiI3lZFz7oWJe6P/W/jwc2/oEtIkLcpVUp4+nS1ylwEW7dulecgCA8Px+zZs9GvXz/s3bsXSqUSa9aswe+//478+fPj/fffh06nAwA4Ojrihx9+wJo1a/DJJ5/Azs4Offv2xeeffw4AsLe3h1qtlq+jVqvlO+UnTpxA3759jfb17t0bvXv3Rnx8PP744w8sWbIEX375JbZv3w4AyJcvn9HxAKDVavH48WPExMTIiTuQOLzBXN53/PhxoyIFACxYsCDNCffDwsJQsWJFo23FixfHkydPULBgQbi6uuLKlSu4cOECvL29ER4ejsuXL8PX1xfNmjUDAISEhGDlypVYu3at3IZGo0FISIhcIChUqJDRNUqXLo0JEybIMRw5cgTfffcd8ufPj549e6Z6b2xsbOT3Wd+T4eDBg/J+rVaLkJCQdN+b12W2QFC0aNHXLg4AiV0q7OzsjLbZ29sjJiYm1bHvvfdeuu3Y29vLz/VtxsbGWhTHm87eSEREuVNsbCx/NxARUaYoXvl9VGncAn+fOgpIEiAEqjZpadWJCvWcnZ0xcOBAdOjQAWFhYdi1axdu376NI0eOIG/evBBCwNPTEwDw8uVLJCQkYPXq1dBoNDh//jy++OILs6sXxMXF4e+//0atWrUAJA4P37ZtG/bs2QMgMfmvW7cubGxsMHDgQLMxFypUCAUKFMC5c+fkbWFhYWZzvxMnTmDs2LFm29crWrSoPBeB3qNHj+Du7g4AaNKkCc6fP4/Lly/j66+/xn///YdLly7h8uXLWLdunRzrJ598Iif2AHD37l0UK1YMoaGhACD3CgASe3e0bNlS7mHv4uKCnj174ubNmwgICDAbc6FChdC3b19888038rb79++nm0u/CbMFgrp162LevHlo1qyZUcKv70qRFgcHB8TFxRlti4mJgYODw2sFaG9vb/QFoX9saTv6ChoRERGQWDjm7wYiIsoszT4djMr1GiH8aYjVVzEwFB0djW3btqFMmTLInz8/IiIiYGNjA5VKhZiYGKxcuRIRERGIj49HWFgYBgwYgHXr1qFOnTooXLgwJElCvnz5jCbQS+nChQvw9PSUu/I3atQI8+bNw3fffYeBAwfCxcUFjx49wsaNG+U77+mpXr06HB0dsXLlSgwcOBCRkZEYOnQoypQpg7lz55o859WrV3j8+LHZ/NRQp06dMGjQILRt2xaenp44fPgwrl69ivHjxwNI7OE+YMAA5MuXD0WLFkXdunXxv//9D++9957c86BTp05YtWoVPD09Ua5cORw4cADjx4/HgQMH5PfDUJs2bbBixQqUKFECDRs2BAD4+vri9OnTab62lDGPGDECzZs3R/Xq1XHp0iV8/vnnWLt2rVGPi7dltkCwe/duAMDhw4flbZbMQVC+fPlUM04GBQWlO3bElAoVKuDevXtyVSooKAgFCxZE3rx5X6sdIiIiIiKirFDc1S1TCgM9e/aEQpE4D71KpUKtWrWwZs0aKBQKfPrpp/Dz80O9evXg4OCAhg0bol69evjnn3/Qtm1bTJ8+HZMnT8bz58+RP39+TJo0CRUrVpQn6DMl5cSARYoUwdatW7Fs2TK0b98eMTExyJ8/P1q3bo0RI0aYjV+tVmPt2rWYPXs2GjRoAEmS0Lx5c7lbvilnzpxBgwYNXuNdAmrXro0ZM2ZgxowZCAkJQZkyZbBixQo5+a9SpQrUarWceFeuXBl2dnZGr7VTp0549eoVhg4diufPn6NUqVJYsWIFypQpk6p3AgD06dMHdnZ2WLlyJcaMGQMhBMqXL48pU6agfv36ZmP29PSUJ1Z89OgRChUqhG+//TZDiwMAIAn91JQZLD4+Hi1atED//v3x8ccf48iRI5g8eTKOHTuW5hqPjx49QrNmzXD16lW5ALBlyxZs2bIF69atg729PYYNG4aaNWti9OjRZmNwdXW1qLsGERG9O9iDgIiIUmLeQJQozR4E69atw6BBgzBz5kyT+ydNmpRuw2q1GuvWrcPUqVOxdOlSlChRAitWrICLiwt+/fVXTJ06Fb6+vmYD/OijjxAaGoqePXsiNjYWrVu3xpdffmn2PCIiIiIiIiKyXJoFAicnJwB4q5UCKlWqhJ9//jnVdh8fH/j4+KTaXqJEiVSVO4VCgREjRljUJYWIiIiIiIiI3kyaBQL9bIzDhw/PtGCIiIiIiIiIKGsosjoAIiIiIiIiIsp6LBAQEREREREREQsERERERERERGRBgeDu3bvYuXMnhBD46quv0Lx5c1y6dCkzYiMiIiIiIiKiTGK2QDB16lTY2tri1KlTePbsGWbNmoXFixdnRmxERERERERElEnMFgji4uLg4+ODc+fOoU2bNvDy8oJGo8mM2IiIiIiIiIgok5gtEMTHx+PFixc4deoUvL298eLFC8TFxWVGbERERERERPQazp49i379+sHLywt16tRBnz59cOXKFXl/XFwcvL29odFoEBsbKz8mAiwoEPTo0QNNmjRBrVq1UKFCBXTt2hV9+/bNjNiIiIiIiIhyvLj7LxH1xzPEBb+y6nV27dqFMWPGoHfv3jh79izOnTsHHx8ffPbZZ7h27RoA4Nq1a/Dw8ICNjQ2uXLkiPyYCAEkIIcwdpNPpoFAk1hL+++8/5M+f3+qBZQRXV1cEBARkdRhERJSN+Pn5wc3NLavDICKibMSaecN/+wIRfe0ZIAEQgEPtwsjfsUKGXycmJgYNGjTA3Llz0bx5c6N9q1atglKpxKpVqxAfHw+FQgGVSiU/bt26NebPn5/hMVHOY7YHQVRUFGbOnIm+ffsiPDwcixcvRlRUVGbERkRERERElGPF3X+J6GvPIDQ6iHgdhEaH6GvW6Ung6+uL+Ph4NGrUKNW+IUOG4LPPPoOvry88PT2xceNG+Pr6onbt2ti0aROLAyRTmTtg5syZKFSoEEJDQ2Fra4vIyEhMmTIFCxcuzIz4iIiIiIiIspWYv18gPsT8TVPNk0iIBOMO20InEHH6IWKLOpo9X10sD+yrvGdRTGFhYciXL1+6wwV0Oh38/PxQrVo1aLVa+Pv7o1q1aha1T+8GswUCPz8/zJkzB6dPn4a9vT0WLFiA9u3bZ0ZsRERERERE2Y59lfcsStzj7r9E3D/hELrkIoGkkODUqCRsS+fN0JgKFiyI8PBwaDSaVEWCiIgInD17FpMmTUJsbCzq1auHhIQEecLCZs2a4bvvvsvQeChnMlsg0M89oJeQkJBqGxERERERERmzLZMPDrULp5qDIKOLAwDg4eEBOzs7nD59OtUcBPPnz0dwcDAmTpyImzdvYtq0afjll1/kx0R6ZgsEnp6emD9/PmJjY3H27Fls2bIFXl5emREbERERERFRjpa/YwU4uBeC9kUMVO/ZW6U4AABqtRqjRo3ClClTIEkSGjVqhPj4eGzfvh179+7F+vXrcezYMVSuXBlAYk9x/WMiPbNdAUaNGgUHBwc4OTlh8eLFcHV1xZgxYzIjNiIiIiIiohzPtnRe5KllnZ4Dhnr06IFJkyZhzZo18Pb2RqNGjXDq1Cl8//33qFOnDgICAuSVfAwfE+lZtMxhTsVlDomIKCUuc0hERCkxbyBKlOYQgy+//BJLly5Fhw4dTO7fv3+/1YIiIiIiIiIiosyVZoFg0KBBAICxY8dCrVZnWkBERERERERElPnSLBBUrVoVQOKMl/v27cu0gIiIiIiIiIgo85mdpNDe3h5Pnz7NjFiIiIiIiIiIKIuYXeYwJiYGzZo1Q5EiReDg4CBv5xwERERERERERLmH2QLBxIkTMyMOIiIiIiIiIspCZocY1KlTB3Z2dggKCoK7uztsbGxQp06dzIiNiIiIiIiIiDKJ2QLB7t27MX78eKxfvx4REREYOnQoduzYkRmxEREREREREVEmMVsg2Lx5M7Zv3w5HR0cUKFAAu3fvxg8//JAZsRERERERERFRJjE7B4FCoYCjo6P8vGjRolAqlVYNioiIiIiIiCzn6uoKOzs7KBSJ94AlSYKHhwfGjh2LSpUq4fLlyxg2bBiuXbtmdN6rV6/g6emJ48ePo0SJEujTpw98fX1hY2MjH1OmTBkMGTIELVu2zNTXRJnPbA8CZ2dn+Pn5QZIkAMCvv/6KfPnyWT0wIiIiIiKi3ODBgwf466+/8ODBA6teZ9u2bfD19YWvry8uX74MV1dXDBo0CAkJCa/VzqhRo+R2fH19MWzYMHz99df4559/rBQ5ZRdmexBMmDABX375JR48eID69evD1tYWK1euzIzYiIiIiIiIcrRDhw7B19dXfu7h4YG2bdta/bo2Njbo3Lkzvv/+e7x8+fKt2mrevDkcHR1x9+5dVKxYMYMipOzIbIGgfPny2LdvH+7fv4+EhASULVvWqLtJdhf9py8canpkdRhERERERPSOefDgAXx9faHRaORtvr6+qFq1KkqVKmXVa798+RKbN29GxYoV4eLiAgCIiIhA7dq1jY4TQqTbTlxcHH799VfExMSgRo0aVouXsgezBYIjR44YPb9//z4A5JjxJw8GDIBzly4oMnlSVodCRERERES5gJ+fH54+fWr2uKdPn6bq3p+QkIDz58/j7t27Zs8vUqQI3NzcLI6rV69e8nxxarUa1atXx7Jly+T9Tk5Oac5BYGjRokVYvnw5gMS5DMqXL4///e9/KFq0qMWxUM5ktkCwefNm+bFGo0FAQADq1KmTYwoEIiYG4bt3I2+7duxJQEREREREb83Nzc2ixP3BgwcICgqCTqeTtymVStSrV88qPQi2bt36WgWFtHz99dfo16/f2wdEOc5rFQgAIDAw0KgKlRMIrRah69cjqnJls8dK9nZQ5MkDhYND0r+kx3kcDLY5QLKzkyduJCIiIiIiSqlUqVLw8PCAr68vJEmCEAIeHh5WH15A9KbMFghSqlChAoKCgqwRi9VIKhUKDBxotgeBEAIiNha66Ojkf1GJH7Uv/pW3ieho6GJiLbu4EJDUatNFBsMiRB4HKOztIXEJSSIiIiKiXKNt27aoWrUqwsLC4OLiwuIAZWuvNQeBEAK3bt2CSvXadYUsI9nbw/nDLhYNL5AkCZK9PRT29kCBAhkWg4iPNy46JP1LePZMLkAk/osCdOlPEiJTSMm9G0wUH4wKEWp1hr0WIiIiIqLcJPrPP61+jVKlSrEwQDmCJMxMW9mnT5/kgyUJLi4uGDJkCFxdXa0e3NtydXWF78/bcuXcAyIhAbqYmKQCQ5TcsyEhKiqxh4PBPxGvMd9gEoWDvUHPBg6xIKLcx8/PL0PGZxIRUc73dMZMhO/ejY7RUQgICMjqcIiy3GvPQZDT5MbiAABISiWUjo5QOjpmWJtCp0s9xCI6GrqoKGj//Vfu5aCLjoZIb4iFYeFACEi2tun2dJDkQoQ9JIUiw14PEREREeU+QquFLjYOIi428W/XuLjEj7FJz5P2yR9jYhOPjdck/50qBDRPnuDl/v2AVpu1L4goGzFbIBg/fny6++fMmZNhwVDWkhQKufdARhFCQGg00Jno2aB58sRongddTLTlQyyUitS9HNIqQNjYZNjrISIiIiJjIiHBTKIeCxEXJyfqifvjIDQaQN+ZOWXP1PS2q5RQ2NpBsrOFws4Okq0dFHa2kOzsobCzhcopb9Jzu8QbVfb2UNjaAjY2Rj1gw/fsxavff4dggYBIZrZAYG9vD19fX3Tq1Ak2NjY4dOgQHB0dUadOncyIj3I4SZISJ2lUq4H8+TOsXaHVJg6xMCwwREdBGxoK8fDhmw2xkCQoHOxNTCJpMJGkfp+tLYdYEBERUbYk9wpNM1GPSfyoT9Tlu+1xr5eo67crJJOJuv656r335ERdsk1K6u3sIKVI2DObunSp5NdBRAAsKBD4+flhy5YtcEi6q9y1a1f07t0bn376qdWDI0qLpFJB6eQEpZNThrUpdDoIfdHB8F9kJLTPnhsXHWItXMUCIvGXI4dYEBERvbPkhN3wDnt6iXpsTOLH+HiDRl7jTrtCgsLWOFFPvttukLAbJfV2kNTqd+oGiEPNmnDu0gXhu3cD0VFZHQ5RtmC2QBAaGgpbW1ujbVFR/Aai3EdSKCDlyQNFnjwZ1qYQInkVi6QJJfVDLTTh4cbLacbEWFzFllRK070cUhYh7O05xIKIiCgFodMZJObJCbvR3Xa5O3yKO+yGv6sNxrMbSbldoTCZqOvvpKtcXIwSdYWtLSR7+3cuYc8KRSZPQt527YCPemZ1KETZgtkCQd26dTFw4EC0b98eQgjs2bMHrVu3zozYiHI8SZIS/wCwtc3YIRYaTfIQC4NhFtoX/0L3IFpe1UIXHQ2hTUhxchpFCIUicYxeer0c8iQvnck/WIiIKKMYJez6RF3/MVWinni3XX8shLA8UddTKCDZqo3HsdvZyXfUlc7OUBVJTtQT78bbcYhhLpVbJzUnehNmCwSTJk3Cli1bcPToUdja2qJr167o3LlzZsRGRGmQbGygtLGBMm/eDGszcenMWKNeDrroaCS8egXd06cGcz1EJ/5BZmmsdnbGBQcTBQiFg0PinRIOsSAiyhaEEKkT9fTGs8fGQRcXCxEXn5iMW5qoGyTbiXfUbY0Sdf1dd2W+fFAVMU7U9Xfh+buDiCjjmC0Q2NjYoF+/fujXr18mhENEWSVx6cw8UDpm8BCLuLgUPR0Sl8rUhoWlWNniNYZY2KjSGWJhMKGkvT0kldkfc0RE2Z5+yNqbTDwHnS65IUvvtOt7wBlMNJfcPd4eyrx5oSpUyKibPBN2IqKcj385E5HVSJIk/9EIF5cMa1fExxsPsUgqPmj//Re64GC5CKGLjgYSdBY0KBKHWJjr5ZBUgMjqWZeJKOsZJ+wWzhCfNPGcnLBL0mvdaZfU6lSJur57vMLRMWniOTso7O2Sx7Hb2kJSKq3/hhARZaLY2FhERkbivffey+pQch0WCIgox5HUaijVaijz5cuwNhOHWMQY9XIQ0dFICA+HJiTEaK4Ho1mlzcVqb2fcwyGt1Szs7Vl0IHoLQggIjSbFHXYTE88ZzhCflNRDZ6JLPGA6gddvR+Jwr9Tj15MS9jyOUBUokDyLvD5ht7Njwk5EVjFw4ED88ccfAID4+HhIkgSbpMmqa9WqhfXr12f4Nc+dO4fly5cjICAACoUCbm5uGDp0KLy9vV+7rdWrV+POnTtYtGiR2WN79+6NwYMHo3nz5m8SNqWDBQIiIuiHWDhC6eiYYW0KIRITkxQ9HRInlHyRPKdDdDR0MbEWDrEQiXcRU00iaWIlCw6xoCwkhAA0GugsniFeP/FcjGU9f0yQ1DYmx68r7GwThx8VcDFO1PXH8PuEiKwsPPwaYmKCYe9QBs75alnlGoYFgHHjxsHJyQkTJ060yrUA4MGDB/jiiy+wYMECNG7cGDqdDr/++is+//xz7NmzBxUqVHit9gYPHmzxsWFhYa8bLlkozd+I5j5Bq1evzvBgiIhyE0mSEme/trcHChTIsHblpTNT/NM+f2Y0maQuOsqyREuSEtfMNtPLQS5IqNUZ9loo88gJu8UzxCdPPAetFsDr93CR1DapEnX9HXVFHgdILvmTk3l7JuxElDsFBExDyJNdSPw5KlCsaFe4uk7LtOvv378fCxcuxMmTJ+XeihMmTEDevHnRpEkTTJkyBY0aNcKuXbuQL18+DB8+HB9++CEA4OXLl5g7dy7OnDkDlUqFjh07YsSIEVCpVLh16xacnJzQuHFjKJVKKJVKfPjhh3jw4AH+++8/AIk9GZYsWYK9e/dCq9XC29sbM2fOhKOjI1xdXfHxxx/jwIED+Oijj6BSqeDn54eVK1di2bJlCAwMRFRUFK5du4ayZcti6tSpcHd3x7BhwxASEoKvv/4aX3zxBQYNGpRp7+W7IM3fwK1atcrMOIiIyELyEAtn5wxr02iIRVSUXGDQhoVBPHpkVIgQ8RqL21U42KcxmWSKooOdXaYMsYj+80/g4iVEx8Rmi2WthGHCbskM8Un7hEb7RteTbFRG49f1Xd8lW9vEBN3Z2ThR199tT+qiSkREryc8/BpCnuyCThcjbwt5sguFi3SwWk+ClJo3b44pU6bgzz//RK1atRAfH4+jR4/ixx9/xKtXr3D//n14e3vj4sWLuHHjBgYOHIhy5crBw8MDY8eOhVqtxu+//47o6GiMGDECa9aswbBhw+Dl5QWtVovu3bujdevWqF27NqpUqYKRI0fK116+fDnOnz+PXbt2wdnZGSNHjsR3332HGTNmAAAiIyNx7tw5xMfHY+PGjUZx//7775g3bx5WrVqFzZs3Y8iQITh69ChWrFiBpk2bYsKECRxiYAVpFgjSWspQCIHg4GCrBURERJnPKkMsdLrUQyyiEwsQ2n//lYsQuuhoiJhYS1uFpLY1XWQwLELkSRpiYTDW++mMmQjfvRsQAg/Wr4dzly4oMnmScetardkZ4k2NZxcaDd7kDjtUyuSJ5gzXY9ffUc/nnGr8usLOjgk7EVEW+/ffI4iI8DN7XESkH4QwLuoKoUVw8DqEOZ4ze76TkxsKFmz5xnECgL29PVq2bIlDhw6hVq1aOHPmDAoXLgw3NzdcvnwZ9vb2GDt2LGxtbeHp6YkWLVrg4MGDKFmyJE6ePInz58/D0dERjo6OGDZsGCZMmIBhw4ahQIEC2Lt3L7Zs2YLffvsNixYtgr29Pbp27YpRo0ZBrVZj//79GDNmDIoVKwYAmDlzJl69eiXH1rZtW6jVaqhN9E709PSEj48PAKB///744YcfcPHiRbRo0eKt3g9Kn9k+fNu2bcO8efMQE5Nc9XJxccH58+etGhgREeVskkIhJ+0ZRT8RnS4qKsUymdFIePrUaJ4HXXS0PFu85skTvNy/P6mrPCAA/Ld9O3TR0bApWjT5Aiql6URdf0c9bz7jddqTEndwZQsiondKwYItLUrcw8OvISzsHIRI7n0nSSqULj0o03oQAEDHjh0xevRoTJgwAfv375cTbwAoVKgQ7Ozs5OdFihTBo0ePEBISAgBo3bq1vE8IAY1Gg7i4ONja2qJQoUIYOXIkRo4ciYiICJw+fRpz586Fra0tvvnmG7x48UIuDgBAwYIFUbBgQaNrp6V06dLyY0mSULhwYfz7779v90aQWWYLBGvXrsXGjRuxatUqfPXVVzh58iSePn2aGbEREREZkfRLvanVQP78Fp8XvmcvXv3+O4Q2+S6OpFbDoU4dOHfuZIVIiYiIAGfn2ihWtCtCnuyCBAkCAsWKdcvU4gAAfPDBB1AoFLhw4QLOnDmDcePGyftCQ0Oh1WqhSpr/JSQkBEWLFkWhQoWgUChw9uxZ2NvbA0gcEhAaGgpbW1uMGjUK+fPnlydCdHJyQvv27fHgwQP89ddfAIDChQvj6dOnqFGjBgAgMDAQJ06cwGeffQYA6RbXnz17Jj8WQuDJkycoaljUJ6tQmDvA2dkZNWrUgJubG0JDQzFkyBBcvXo1M2IjIiLKEOrSpQAhEJ63HJ4U9sLLvGUBIaA2uDtBRERkDa6u0+Dh8UPyx0pTMz0GhUKB9u3bY8GCBahWrZpRoh0ZGYkVK1ZAo9Hg4sWLOHHiBHx8fFCkSBHUqVMHc+fORVRUFCIjIzF+/HhMmpQ4PK9NmzbYuXMnfvnlF8TExECr1eLmzZv49ddf5bkBOnTogDVr1uDZs2eIiorCokWL8OjRI4tiPn/+PE6fPg2NRoN169ZBoVCgbt26AAAbGxtERERk8LtEgAUFApVKhZcvX6J06dK4ceMGACAhIcGixv39/dGjRw+4u7ujQ4cO8vkphYSEoH///vDw8EDz5s1x+vRpeV9CQgJmz56NevXqoU6dOhgyZIhRNcmcJ3dfWnwsERHlTg41a+J+06/xV43hCKjUA741vsD9pl9ni4kKiYgo93POVwtFi36Y6T0HDHXs2BH+/v5GwwsAIE+ePHj58iXq16+PadOmYd68eXBzcwMALFy4EJGRkWjRogWaNm0KSZKwZMkSAECzZs2wYMEC/PLLL2jQoAFq166NCRMmoF+/fujevTsAYMiQIfDy8kK3bt3QtGlT5MmTB2PHjrUo3urVq+PHH3+El5cXTp48ifXr18tDIbp06YLp06dj0aJFGfTukJ4kRPoLb+/atQu7du3C6tWr0alTJ7i4uKBw4cJYtWpVug3Hx8ejdevW+OSTT/Dxxx/jyJEjmDZtGk6ePAnHFJNg9ezZE+7u7vj666/xxx9/YNiwYdi3bx9KliyJn376Cfv27cPatWthb2+PiRMnIi4uDsuXLzf74lxdXfF1qzVw8y6Khj1dLXg7iIgoN3oSGI5f//cXtPHJyz6q1Ar4fOmBouXzZWFkRESUHbi6uiIgICCrw7CqsLAwNGvWDGfPnpXzscuXL2PYsGG4du1aFkdnbNmyZfKSh5S5zM5B8OGHH6Jt27ZwcHDA9u3bcfPmTVSvXt1sw1euXIFGo0G/fv0AAO3atcNPP/2EQ4cOyRUlALh37x5u3bqFDRs2QK1Wo27dumjatCl27dqFkSNH4t69e4mTUiXVMRQKBWxtbS1+gdp4HfwuPEFFzyJv9UegXEcRiZNbJW00eAx5h0g6yHCfUR1GGHxI2m5UphFJbcjnmojDsB2R/CTVsUbPUxyXRrvywxRxZGQ7yftSvIcm2jV6nwxjSfk+GbTzRu93ytiQup20Pi+m4kvr85IyPpPtpIpFGL2fpuIzfs9Sfu5Nf15Mf90Yvx5Ou0a5wYtHkdAlGNfDdQkCvkeC8bBExq3cYIoAoFBIkBQSFEoJCqOPilTb5edG2xSmjzF4rlQqIKVoX1LwO5iI6F2n0+kQGBiIn376Ca1atUp1s5bIkNkCQZcuXbBnzx4AiZNMFC5cGO3bt8eBAwfSPS8wMBDly5c32lauXDncuXPHaNvdu3dRtGhROBjMcl2uXDl5OEL37t1x9OhR1K1bFwqFAqVLl8bWrVste3VJMuyPwKRJNAzn0kh8nLRBSrnP8AkgWXhcynZTXc9gg8l20ogv8VyD3SnbkU+XUrRh4nopXo+p+KRU+wxev+FrNbxeivjksBSSQbgSjC5t4vPypu93yjZSxpfu59cgPpPtpHofjT8vptpN//OS/MDwrTFqx+h9MtGOifc7+fUwsaDc40lgOB76hRkVCRRKCR4tS2dKDwKhE9AlCOh0if9EgkBCgi55e9K+1M91iY/jtYnnJgjjtuTnOvm5YVtCl24nwdRxJn18k+9+wyu9UUHEoDDy2gURg+1ERGRMkiR88sknKFSoENatW5fV4VA2l2aBoG/fvrh58yZiY2NRs2ZNebtOp0O1atXMNhwdHW20XAaQuAan4XKJABAVFWXyuNjYxDWxNRoNGjZsiOHDh8PR0REzZszAF198gS1btph/dXoS4FJJwKlYnOXn5EImbkBnPyKNx0REb6lwZTs8uR0DSAIQEoq42SE8PgThfiFZHZp5iqR/Jn5rSwCUSf+yCyEERELSRx0gdIBWl/RYv02bvE8k7dOJ1NtEyvN0SCqAJO0TSL6WYa+2t6lyvM65ApAUSStsKJIeKxIfKxQG2+T9KT8CilT7ko5XJp+Xqi0lC7lEZBlJknDp0iWT+7y8vLLd8AIA+OKLL7I6hHdWmgWCFStWIDw8HBMmTMCcOXOST1CpjNauTIuDgwPi4owT8piYGKOeApYcN27cOIwbNw5FihQBAEyZMgW1a9dGQEAAXF3NzyugUivg5l0MHzSrZPZYIiLKvdzcEieu9fsrEG7uFTj3AGUYoUvu0WGyh0iavT90Rs/N9RBJSLH9dXuIZMhrhUEPkRTDYaSkXiLGz9PoIWL0POV+/WMFe4gQEWWyNAsEjo6OcHR0xI8//ojHjx/jypUr0Gq18PLygkJhdvEDlC9fHps2bTLaFhQUhE6dOqU6LiQkBLGxsXJPgqCgIFSoUAEA8OTJE8THx8vHK5VKSJIkr9NpDiegIiIivaLl8yE83oG/FyhDSQoJSoUEpWV/muR4pgoiOn0hw5KCiEZrXBBJWSBJo63MLojor/amBRFJKUH5hgUR/fAZyhxPAsOzOgSibMPsr7KzZ89i9OjRqFmzJnQ6HebNm4c5c+bIa1umxcvLC0IIbNq0SV7FICAgAC1atDA6rly5cqhcuTIWL16Mb775Bn/++SeOHz+O7du3AwAaN26MZcuWoVq1anBycsJ3332HypUro2zZsha9QP4RSERERJRxWBBJXcQwPZ/ImxVEhEFRBMjcUTNv00MkpxZEzmwLgN+FJ1lybaLsyOyP9qVLl+Knn36S7+j/888/GD16tNkCgVqtxrp16zB16lQsXboUJUqUwIoVK+Di4oJff/0VU6dOha+vL4DEZSwmT56MunXrIn/+/Jg1axYqVUocEjBt2jTMnz8fnTt3hkajgaenJ1auXGlRLwYiIiIiorfxzhZE9IWKTC6IpIon6aM1ygevwmLxz5VnqVa5IXqXScJoHbfUOnbsiH379hlt69ChA/bv32/VwDLCu7CeKRERvR4/Pz+4ublldRhERJTF/C8+wZmfA6CJ1+F/J4cwbyBC4pzI6bKzs8PNmzfl5zdv3oS9vb1VgyIiIiIiIrKmfAXtuWgWUQpmO0uNHj0agwcPRunSpSFJEoKCgrB06dLMiI2IiIiIiMgqilZwhpt30UyZg+BKeCTuxcSjnIMtPPPlseq1zp49i++//x5+fn4QQsDV1RVffPEF6tSpY9XrUu5gtkBQq1YtHDx4ENevX4dOp4O7uzvH/xMRERERUY7XsKcrKnoWwaLfrXeNCXceYduTUCTOpCDQs2gBzK5UwirX2rVrFxYuXIgZM2agYcOGAIB9+/bhs88+w/r161G7dm0AwOXLlzF+/HicOHHCZDvLli3D48ePMXfuXKvESdmX2Uy/S5cucHZ2RqNGjdCkSRPkz58fH3/8cWbERkREREREZFXWXPXsSngktj0JRbROIFqnQ7ROYNuTMFx9GZXh14qJicHcuXMxY8YMNG/eHGq1Gmq1Gt26dcPnn3+Oe/fuZfg1KfdJswdB3759cfPmTcTGxqJmzZrydp1Oh2rVqmVKcERERERERNnNb/+G41ZkjNnj/o6MgSbFRAcaIbDiwTNUcTQ/r1tVR3u0KehsUUy+vr6Ij49Ho0aNUu0bMmQIAODzzz/HH3/8gYSEBMTExMg9Cj777DN89tln8vO4uDgIIXDs2DEAwOrVq+V9lLulWSBYsWIFwsPDMWHCBMyZMyf5BJUKBQsWzJTgiIiIiIiIsps2BZ0tStyvhEfiTFiEUZHARpIwrFThDJ+LICwsDPny5YONjU2ax6xZswZA2kMMrl27BoBDDN5laRYIHB0d4ejoiB9//DEz4yEiIiIiIsoV6jg7omfRAtj2JCxpBgLgo6IuVpmosGDBgggPD4dGo0lVJIiIiICtrS3UanWGX5dyF7OTFBIREeUWj/1v49FffyCvAiju6pbV4RAR0TtgdqUS6Fw4P4Ki46y6ioGHhwfs7Oxw+vRpNG/e3Gjf/PnzERwcjB9++MEq16bcQxJC5NrlP11dXREQEJDVYRARUTZwfMNq/H3qKHQ6AYVCQpXGLdDs08FZHRYREWUDuSVv2L59O5YuXYoZM2agUaNGiI+Px/bt27F48WKsX7+eSx2SWWmuYjBx4kQAwG+//ZZpwRAREVnDY//b+PvUUWji4pCgiYcmLg5/nzqKxwF+WR0aERFRhunRowcmTZqENWvWwNvbG40aNcKpU6fw/fffszhAFklziMGFCxfw559/4n//+x9Kly6NlB0NqlSpYvXgiIiI0iKEgCYuFrGREYiNjERMxCvERkYmPY+AVqOBJCUe+/x+EBK0WqPzE7QJuPrrLtwvXU4+zpgEG1tbqGxtYWNrB5ukjyq1LWzsUmyztYWN2haSwuzqwURERFbVtm1btG3bNqvDoBwqzQJB9+7dMWbMGDx9+hTDhw832idJEo4fP2714IiIKPcTQiA+Jjo50U9K8PXJfsrEHoCc0NvY2cPO0RF2jk6wz+OEAsVLwM7RCbaOjrBR28rHP/a/jQc3/4IuIUHeplQp4enTNc25CIROB218PDRxsdDExSV9jIU2Lg4xr17hVdy/8nP9MYnTTxm+NuN4U1KpbZMLDmpb2NglFSBs7WBjZ2e8zc4WCoXS8jeWiIiI6DWZnYNg5MiRWLx4cWbFk6Fyy1giIqKcQOh0iI2Oku/gG97Nj42KhNDpko81+M0jSYDa3gH2TnkTk/08TrBzdEpK/B2hVKW9XNPrkOcgEAIKSULVJi3RtP/nGdL2mxBCQKuJhyY2Ftr4OGhi45I+JhYiNEmPk7fFQQid+Ybl9gGVjU1SL4gUvR3k58bblCrOXUxE7ybmDUSJLJqk8NChQzh79iw0Gg3q16+PTp06ZUJob4/f6EREr0+XkIDYqEjjBF9O9KPkIWep74pLsMuTJym5d5Lv7Ns5OsEujyMUyqy/+/04wA+3/7yG92vWzvWrGAghkKDVpurloImLhTY2qQARF5e8LzYWCQkJafZ2SN0+oFQqE3s6yEWGxJ4QKoPeDykLEJKlFyAiykTMG4gSmb1VsGHDBuzbtw+dO3eGEAIbN25ESEgIhg4dmhnxERHRG0rQapKS/KREPyr5rn5cdBTSKg8rlArY5UlO7u3z5kX+YsUTu+47OOTobu7FXd3wSvduLHEoSRJUNjZQ2dgAjk5WuYbpAkTi49iICHmbNuljglZjsp20hmJIkiKx6CAPs0gqPBj2hLCzlfepbNQsQBAREb0FswWCPXv24Oeff4ajoyMAoGvXrujevTsLBEREmUSr0SQm+EmT8MVEJXfhj4+JSZVU6ZMtpVJpdCffMX8BvFeyNOwcnaC2s+eEevTWlCoVlCpHII+jVdrXJSQkFhj0wywMhmDEhUcZbEs8RhsfZ7KdtOeCSDkRZfLQi9Tb7KBSq/l9Q0REuZpFgw31xQEAcHJygopjFImIXosQAtr4uBTd9pMn5NPExQIwzl70yYxSZWPUbT/ve4VQuEx52Dk6wsbOnndMKddSKJWwdXCArYODVdoXOh008XGph2DIE1E+N9qWOA9EWkNsTDOaiDLVahiGQzESixI5uYcOERHlfGYz/eLFi+OHH35Ar169AABbtmxBsWLFrB4YEVF2JISAJjbG5Gz7iTPuawyONT7XxtYWdo6OsHdMnIzPuUhRFHGsBDtHR6jUtkz0iTKZpFBAbWcPtZ29VdrXFwaT53pILjbERUUiMuyFPCxDPwxDlzSZp6kfByl7QqSaiDLVEIzkiSj1wzQ4ESUREaXH7G+J6dOnY9SoUZg3bx4AoEaNGliwYIHVAyMisqZUS+tFvDJeWi9pOTxTf6SbXFrPKS/s8jhCpVZn8ishouxKkiR5mII1GE5EabTiRXwcNDHRiA7/L9VqGDpdQjrt6eNOfq5UqVKsdmFqNYzkySg5ESURUc5mtkBQuHBhbN68GTExMdDpdMiTJ09mxEVEZBGdLgFx0dFIa2k9XYLpZeEkSYKtg0PyGP08TnAqUDDDl9YjIrIWw4ko7a08EaXhMAt9T4iYiFfy8Az9Pn0vqrTnfUjxGhQKeZiFymDlC+NtnIiSiCizWNzPzN7eOt3viIgAw6X1TCX6UQCM++sn//GZcmk9J+QrVCQp6c8eS+sREeVU+oko7aw8EaWp5Tjj/gtNmpQyuSiRoIkHkH4BwniflLrXg51t6tUwOBElERGA1ygQEBFZwtTSejEREUlL60WnOl7/x52kyL1L6xERkWmZNRFlqiEYsXGIfvVSHp6RfEw8APEaBYjEiSiNV74wHnaRcnUM/j4jouyMBQIiMkkbH288276JpfVSTsIHGCyt5+TEpfWIiChLZf5ElMk9IGIjI6AJ/deoB4Q2Lg5C6AzOT4pTgslt8kSUdilWvkjqCZFyNQxORElEb8uinyBXrlzBy5cv5aV9AKBly5ZWC4qIMobh0nqJk/BFItYg0dcvrZfyDon+DxKTS+s5OcHG1o5jQImI6J2XKRNRajRyDwfDoRjx0dGI+u+/VEt0Gk5EaUlPiMSJKI17OZjuAZF7J6J87H87q0MgyjbMFggmTZqEM2fOoHTp0vI2SZJYICDKROaW1tNqNCbPk6TEro92jk6wT0r08xcpJif9XFqPiIgo+5IkCSq1Giq12ooTUWrkAoMmNs5oKEZ0xMvknhFJ+0wt55teAUKhVKQqMhhNRJlidYzMnojy+IbV+PvU0Uy7HlF2Z7ZAcPHiRRw6dAiOjtaZnIboXSJ0OsTFRBtPwGeQ7Juq+uup7Q2W1nN0QoESJRMTfS6tR0RERG9IqbKBUmWTKRNRppyMMjYsFFqDHhCauDh5IkpD6Q3FUCgUiUWGFCtfJA/FMB6eYTgR5WP/2/j71FFo4uKs8tqJciKzBYKiRYuyOECUQqql9ZIm4YuJjERcVCR0uuTxhcZFcC6tR0RERO8Oa09EqdMlQBsfbzwRZdK8D1Evw6H9N3l4hiYusReEEIl/nz2/H4QErdYqcRHlVGYLBDVr1sTIkSPRpEkT2Nklj6/iEAPKDcwtrSdMzcKHpKX1HB2NEv18hYtyaT0iIiKiTKRQKN94IsrH/rfx4OZf0CUkmD+Y6B1htkDg6+sLANi5c6e8jXMQUHaTvLReRNIY/eREX7+0nsluacrUS+u5FCshL63HGfeJiIiIcqfild9HlcYtOAcBkQFJpHWLNAWtVgshBGxsck4XaFdXVwQEBGR1GPQa0lxaLyIC8bGxJs+RJEChVCXfzTeYkI9L6xFRSn5+fnBzc8vqMIiIKJt4HOCHpj6dmDcQwYIeBKGhoRg7diwuXbqEhIQEeHp6Yv78+ShcuHBmxEc5kBAC2rg449n2jZbWi5Pv5qcsTxkurWfv5IR8BQujcNnysHPk0npERERElPGKu7JoTKRntkDw7bffwt3dHYsWLUJCQgI2b96MadOmYdWqVZkRH2WhVEvrRUQYJfopl9YzzN3TW1rPWmsFExERERER0ZszWyC4f/8+li5dKj8fMWIE2rVrZ9WgKGOZW1ovIY2JWSQJUNvZy3f07RwdUaBEKS6tR0RERERElAuZLRBotVrExcXB1tYWABATE8Nu3llEp0tAXFRUqtn2DZfWMzURnyQBtg55jJfWe68g7B2dYJvHEUqV2S8DIiIiIiIiyuXMZoZt27ZFv3790KVLF0iShF9++QWtWrXKjNhyLVNL68VEvEJsZCTioiNTjcsHEpN8SVLANk8eLq1HREREREREGc5sgWDYsGEoUqQIzp49C51Ohy5duqBr166ZEVuGeBzgZ7WJR9JbWi8+Jtpkog8YL61n7+gEh7z5uLQeERERERERZak0lzmMjIyEo6MjwsPDTZ7o7OxsxbAyhqurK4Z5VUWVxi3Q7NPBaR6nX1ovxsT4fMOl9VLOvK9UpbW0Xl6o7e05FIOIKBviModERJQSl0cnSpRmD4I+ffpgz549+OCDD4wSXSEEJEmCn59fpgT4tjRxcbhx7DA0cbFwKlDQ5DEqtdoguXdCvkJFULicI5fWIyIiIiIiondGmgWCPXv2AABu374NRYou72n1KsiulCoVSr5fDVUaNcvqUIiIiIiIiIiyJbOD3T/88MNU23r37m2VYKxHwLlIsawOgoiIiIiIiCjbSrMHQd++fXHz5k3ExsaiZs2a8nadTodq1aplSnAZwcbWFlWbtLTaRIVEREREREREuUGaBYIVK1YgPDwcEyZMwJw5c5JPUKlQsKDpsfzZ0YcTZ7I4QERERERERGRGmkMMHB0dUaJECaxcuRIHDhxA8eLFAQDr169HrMHM/tkdiwNERERERJSWuPsvszoEomzD7BwE48ePlyclzJs3LyRJwuTJk60dFxERERERkVX9ty8QL76/ldVhEGUbaQ4x0Lt//z6WLVsGAHBycsKECRPg4+Nj9cCIiIiIiIjSInQCEAIQBo91KR4LAegSj4FOGD2PexSBqKtPAa3I6pdClG2YLRBotVpERkbC0dERABAVFZX4jZVDxAW/gm3pvFkdBhERERG9Q4RITkohBIQOSUmrSEpgkZSwGh5jsD1lUmu0PUXym9S2EAaPdSkeGybSKbfrk+ocRpKS/lMkfpQUkvzc6LFkervmcWRWvwSibMdsgaBTp07o1q0bWrduDUmScPToUXTp0iUzYssQL9bfhEPtwsjfsUJWh0JERFks7v5LqAJjEefA4jG92+SEM1WimSJh1T9OlcgmPza+e5siEU4rQTZqNzlhTe86Oej+lCwxGQWgkBKTVMNE1iipTWt70nP9+fq2FApAldh+YruJ+1JfzyBBtuA6kiRl1VuVJZROakRfewaBHPjFRWQlZgsEn3/+OSpUqICLFy9CpVJh1KhRaNSoUWbEliGERofoa89gX9kF6pJOiRv1P2CBpI+SweOkaiQMjjE8LsW2d+0HKRFRTvXfvkBEX3sGO6HDiyssHmd3qe6Emug6bHTHNEXXYaEzeGxwlzWtY9K7TmIynU7XZV0OTV7lu68SJIXB46QEM2VSarRPSuMc/XaFApAkKAwS1jTPSZk8p7Vdwb+5KGPZlskHh9qFEX3tWVaHQpRtmC0QAECzZs3QrFkzAIm/DO/fv48yZcpYM64MJXQCUb7Pofk3JmlD0n+Gv8yTtsm/4IXBDmHw3OAvgJz4xwDlTHIdyrAgJcFk4SpVgUsyOEFK/dBcwSzxWMsKZpAkoybSurb+iZRym4n2DNuRkHqbqfOllNdN6/2DQZHPqD3T26RUr8vEsUnPJRPbkl9XcmyGoZr+vBm0mbK9dN4/iwqdKd+DXCzu/svEu0QaHSQAAonFYwf3Qm/Uk8CirsMp736a6jpscnt642YNztGlOD+tLsVyIp0hb2Wmeduuw8l3T6Xkgr6+LZVCTlDlc/SPje7ESsbJs9ExxskubxoQ5Uz5O1aAg3shYEdWR0KUPZgtEPz888+YP38+YmJi5G0uLi44f/68VQPLSJJCgmPdYuxOSjmSnIgAqQtX8jaRdGyKbSmLYCLFcYZPTBTM5CYsKJglH2uqvdTbhLDsWJHiGqaum7LAJ+RtOhPvXeIBRq8rZRwm3j+RTmEx5TXk98HM503A/HXlxC/VazB97dSFToNrvMOFTs2TSIgE4xcrdAIRpx8itqjjG7WZ7l1O+U5oGtsNuw4bdjF+067DFlyHySsRkWnMEYiSmS0QrFu3Dhs3bsSqVavw1Vdf4eTJk3j69GlmxJYhJBsFHGoX5jc+5Vhy907jraaPtXo0RDlT3P2XiPsn3GgSLkkhwalRSf5+ICIiIkqiMHeAs7MzatSoATc3N4SGhmLIkCG4evVqZsSWId4bWI1jTImI3nH6caaSjQJCxeIxERERkSlmexCoVCq8fPkSpUuXxo0bN1CvXj0kJCRkRmwZgn/8ERERkDzO9MGNuyhVvTx/PxARERGlYLYHQffu3fH555+jcePG2L59O7p06YLy5ctb1Li/vz969OgBd3d3dOjQATdu3DB5XEhICPr37w8PDw80b94cp0+fNtq/c+dONGvWDB4eHujZsyf8/f0tuj4REZEh29J5oa1gx+IAERERkQlmCwRubm7YsGEDnJ2dsX37dgwdOhSLFi0y23B8fDyGDh2KNm3a4OrVqxg8eDAGDBiAyMjIVMd+/fXXcHV1xeXLlzFjxgyMHDkSDx8+BACcPn0aCxcuxJIlS3Dt2jXUq1cPX3755Ru8VCIiIiIiIiJKi9kCwahRo+Dg4AAAKFy4MJo3bw47OzuzDV+5cgUajQb9+vWDjY0N2rVrhwoVKuDQoUNGx927dw+3bt3CiBEjoFarUbduXTRt2hS7du0CAGzevBmDBw9GtWrVoFQq8fnnn2PRokXQ6XRv8nqJiIiIiIiIyASzcxC4urpi//79qFWrllwoABInL0xPYGBgqqEI5cqVw507d4y23b17F0WLFjVqu1y5cvJwhL///hsNGzbERx99hKCgIFStWhVTp06FQmG2tgEA8PPzs+g4IiJ6N8TGxvJ3AxEREZEJZgsEx48fx+HDh422SZJk9o+r/7d379FR1Pf/x18zs7Ob7CaELCoFke/XEAWLWiMIUrW2KC0iCmp/0h+cWj2iAv6KStV6ftVj/dZbrRaF0qq0igV/BUVRWj1VjlV65FQFiwqWS4VQriomXHPb2Zn5/bGbzW6y4ZbbJnk+zuFkMzvz2c9EYTKvz+fznurq6iYzDfLz81VTU5OxraqqKut+tbW1kqR9+/bp+eef15w5c3TSSSfpscce05QpU7R06VIFAoftvk477bTD7gMA6D7WrVvHtQEAACCLw95hr1mz5pgaDofDqqury9hWU1OTMVPgSPYLBoOaNGmSSksTjyq8/fbbtWDBAm3evFmnnnrqMfUNAAAAAABkanae/j333JN6XVlZedQNDxgwQOXl5RnbNm/enLrRT99v586dqRkDjfcrKSnR/v37U+95niff94+6PwAAAAAAoHnNBgRr165Nvb7++uuPuuHhw4fL933NmzdPjuPotdde04YNGzRq1KiM/UpKSjRo0CDNnDlTsVhM7733nt566y2NHTtWknTVVVfp+eef1/r16xWLxfToo4+qtLRUp5xyylH3CQAAAAAAZNdsQJA+Sn8sI/bBYFBz587VG2+8oWHDhunJJ5/UnDlzFI1GtXTpUpWVlaX2nT17tjZt2qQRI0bo7rvv1gMPPJBaPjBx4kRNnTpVt9xyi4YPH65///vfmjNnjgzDOOo+AQAAAACA7Ay/mbv/8ePH65VXXpEkXXHFFVqyZEl79qtVDBw4UBs2bOjobgAAcghFCgEAjXHfACQ0W6TQ8zzt27dPvu/Ldd3U63qHe8whAAAAAADoPJoNCDZu3Khzzz03FQoMHz489d6RPOYQAAAAAAB0Hs0GBOvXr2/PfgAAAAAAgA7UbJFCAAAAAADQfRAQAAAAAAAAAgIAAAAAAEBAAAAAAAAAREAAAAAAoBvbu3dVR3cByBnNPsUAAAAAALqyDRt+rp27Fnd0N4CcwQwCAAAAAN3O3r2rtHPXYnleTUd3BcgZzCAAAAAA0OV4XlxOfK+cWIVisQrFnAo5sUo5zl5Jvg4cXCffj3d0N4GcQkAAAAAAIOf5vivH2atYrEKOU5m86a+U4+yRfK/pAYaloF0sOxhV0O6lgoJBCtq9ZNtFMgxLe/euUmXlCvm+0/4nA+QoAgIAAAAA7c73PcXj+xI3+rHK1Ah/zKmUfLfpAYYp2+6poB2VHeylSKRUxcFeCgR6yjSP/ramZ8+h6tvn+8kaBIQEgERAAAAAAKAV+L6veHx/oxH+xE1/9qn8hmy7SLYdVTDYS+HwyQoWDZVt95Rp2u3S54EDf67eX7tM0sR2+Twg1xEQAAAAAGjC93257kHFYl8lpvKnjfT7XrYRd0MBu0dqhD8//yQVFZ0l2y6WaQbbvf9HqmfRkI7uApAzunxAsHXrVvXv37+juwEAAAB0qMQNf1XGCH/9V8+ry3pMIFCYGuEP5fVVYeEZsu2oLCvUzr0H0B66fEAwf/58lZWVacyYMR3dFQAAAKBVuW515vr95LR+162WIaPJ/lYgkhrhD4VOUGHh15M3/Hkd0HsAuabLBwSO42j16tU6/fTTmUkAAN3c1q1bVV5erkgkwjUBQE5y3drkqH7atH6nUm68Kuv+lpUv2+6lYLCX7GBUkcgpCgajsqxwO/ccQFfQ5QMCSXJdVytWrNCmTZs6uitIYxiGDMPI+rql7+dqW52l30BX9Prrr2v16tXyPE+rV69mdhmAduF5dU1G+B2nUvH4gaz7m2aegsHECH/QjioSLkmO8Ee4TreRrVu3dnQXgJzRLQICy7J03nnnMVqUQ3zfl+/7R/W6pe+3VVvpf3KpX8fyPnJTZwiVcj1Y27Fjh1avXi3HSRTVcl1Xq1evVmlpqfr169fsz701trdmWy39DG4ugJbzvJgcZ09yKn/DCH/c2S+p6bXUNIOpNfx2MKqi/P4KBnvJsgr4O5kD6sNjAAldPiAoLq7UKaeMJhzIMYxWA0empQFUS99vzbY8z+uwfm3bti31+fU8z9OaNWu0Z8+ejH2z/Tc4mu2t2VZ7fAbQ3fm+K+mgpP1pXw/IV/Yp/YYsSYWN/hwv6b+O4Heb/ck/5a3Sd7TMvn379MknnzS5PgDdWZcPCM448y2d2DcqiWmkADofwrTWccIJJ+hf//qXXNdNbTNNU+eccw4BMtDF+L6bdYTfcfZKftMbQcOwkiP8J8i2BykY7KVgMKpAoEiGYbb/CaDdfPTRR/r0008JCIA0XT4g8P1a7dz1gnr0OEvFxcNkWQWyrLBMs8ufOgAgqX///iorK0vVIDBNU2VlZYQDQCfg+54cZ2/DGv7U1z2S7zY9wLBk2z0TN/p2VJGCgSq2o7LtnjIMq/1PADkrGo12dBeAnNNt7pL37f+nfD+mePygXLdKvu+pYZ2Y0ei1Et8blgJWRFYgooBV0PDViigQqP8akWnyHFgAyHVjxozR6aefrrVr1/JkG6AD+b6neHx/0xH+WEVyun8jhik7UJRYw29HFQkPkN1zmOxATwZ80CLp4TGAhG7yr6qpr31tnHoWDTmqozwvLtetluseTAULcbdKsViF4u4BufEquW6VPC+mjGBBUrbQwQqEE6FCetgQiCRDiEIFrIhMM4/pxADQRvr376+qqirCAaAV+b6vePyAHKciedNfP8JfKd9zshxhKGD3UDD5aL788H+ryD5btl0s07Tbvf/o3urD4z/+8Y8d3RUgJ3T5gMA089W37/866nAgcWxAptlDtt2jxf3wfS8ZNlQpHq9KhQ51tbtU5VbJjR9U3K2S59bqsDMblHgETn3IEAhEZFk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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = all_language.plot(grid=True, lw=0.5, figsize=(14,6), marker='o')\n", + "\n", + "#Show the legend outside of the plot.\n", + "legend = ax.get_legend()\n", + "legend.set_bbox_to_anchor((1, 1))\n", + "plt.title('Fraction of total queries in the year for top programming languages', fontsize = 14)\n", + "plt.xlabel('Languages', fontsize = 12)\n", + "plt.ylabel('Fraction of total queries in the year (%)', fontsize = 12)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are tring to answer question 9. Predicting the growth of languages for upcoming years based on the survey answers (2018, 2019, 2020).\n", + "\n", + "Since we have only 3 years of dataset. There is not enough data to use time series forecasting method to predict the future popularity of programming languages. With very small number of observations, there is insufficient data to split the observations into training and testing. We need more observations to build the predictive model, this question we leave for further exploration in future projects." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Can we predict the salary of Data Scientists?" + ] + }, + { + "cell_type": "code", + "execution_count": 395, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn import preprocessing\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n", + "from sklearn.metrics import r2_score\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.svm import LinearSVC\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 396, + "metadata": {}, + "outputs": [], + "source": [ + "#Rename columns\n", + "cleaned_2018.rename(columns={'JobSatisfaction': 'CurrentJobSatis', 'JobSearchStatus': 'JobStatus', 'YearsCodingProf':'YearsCodePro'}, inplace =True)" + ] + }, + { + "cell_type": "code", + "execution_count": 397, + "metadata": {}, + "outputs": [], + "source": [ + "sal_df = ['Age', 'Country', 'EdLevel', 'DevType', 'YearsCodePro', 'SalaryUSD']\n", + "df1 = cleaned_2018\n", + "df2 = survey_df_2019\n", + "df3 = df2020" + ] + }, + { + "cell_type": "code", + "execution_count": 398, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(191693, 6)" + ] + }, + "execution_count": 398, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Append Dataset 2018 x 2019 x 2020\n", + "df_sal = pd.concat([df1[sal_df], df2[sal_df], df3[sal_df]], axis=0)\n", + "#resetting the index values\n", + "df_sal = df_sal.reset_index(drop=True)\n", + "df_sal.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 399, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8727" + ] + }, + "execution_count": 399, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#creating data scientist scientist df\n", + "all_ds = df_sal[df_sal['DevType'].str.contains('Data scientist') == True ]\n", + "all_ds = all_ds.reset_index(drop=True)\n", + "len(all_ds)" + ] + }, + { + "cell_type": "code", + "execution_count": 400, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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872628United StatesMastersData scientist5180000.000000
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8727 rows × 6 columns

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" + ], + "text/plain": [ + " Age Country EdLevel DevType YearsCodePro \\\n", + "0 28 Canada Bachelors Data scientist 3 \n", + "1 21 Canada No Degree Data scientist 4 \n", + "2 25 Argentina Masters Data scientist 3 \n", + "3 19 Netherlands Associate Data scientist 1 \n", + "4 25 United States Bachelors Data scientist 6 \n", + "... .. ... ... ... ... \n", + "8722 23 Russian Federation Bachelors Data scientist 3 \n", + "8723 27 Germany Masters Data scientist 2 \n", + "8724 47 United States Bachelors Data scientist 22 \n", + "8725 33 Panama Masters Data scientist 2 \n", + "8726 28 United States Masters Data scientist 5 \n", + "\n", + " SalaryUSD \n", + "0 366420.000000 \n", + "1 170292.187500 \n", + "2 8400.000000 \n", + "3 87994.000000 \n", + "4 66750.000000 \n", + "... ... \n", + "8722 33972.000000 \n", + "8723 97284.000000 \n", + "8724 148951.282051 \n", + "8725 72000.000000 \n", + "8726 180000.000000 \n", + "\n", + "[8727 rows x 6 columns]" + ] + }, + "execution_count": 400, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_ds['DevType'] = 'Data scientist'\n", + "all_ds" + ] + }, + { + "cell_type": "code", + "execution_count": 401, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "54049.0" + ] + }, + "execution_count": 401, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Divide SalaryUSD into 2 groups; SalaryUSD >= median and SalaryUSD < median \n", + "all_ds['greater than median'] = all_ds['SalaryUSD'] >= all_ds['SalaryUSD'].median()\n", + "all_ds['SalaryUSD'].median() #56616.0 USD" + ] + }, + { + "cell_type": "code", + "execution_count": 402, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{False: 0, True: 1}\n" + ] + } + ], + "source": [ + "\n", + "#Encoding the target\n", + "labelencoder = preprocessing.LabelEncoder()\n", + "all_ds['gt_median'] = labelencoder.fit_transform(all_ds['greater than median'])\n", + "\n", + "le_name_mapping = dict(zip(labelencoder.classes_, labelencoder.transform(labelencoder.classes_)))\n", + "print(le_name_mapping)\n", + "#{False: 0 (SalaryUSD < median), True: 1 (SalaryUSD >= median}" + ] + }, + { + "cell_type": "code", + "execution_count": 403, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8727, 4)" + ] + }, + "execution_count": 403, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = all_ds.drop(['SalaryUSD', 'greater than median', 'gt_median', 'DevType'], axis = 1)\n", + "y = all_ds['gt_median']\n", + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 404, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8727, 225)" + ] + }, + "execution_count": 404, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cats_lst = X.select_dtypes(include = ['object']).columns.tolist()\n", + "for col in cats_lst:\n", + " X = pd.concat([X.drop(col, axis=1), pd.get_dummies(X[col], prefix=col, prefix_sep='_', drop_first=True)], axis=1)\n", + "X.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 405, + "metadata": {}, + "outputs": [], + "source": [ + "#Splitting data\n", + "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.30, random_state=142)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Training" + ] + }, + { + "cell_type": "code", + "execution_count": 406, + "metadata": {}, + "outputs": [], + "source": [ + "all_metrics = {}\n", + "\n", + "def metrics_data(title, labels, predictions):\n", + " \"\"\"\n", + " INPUT:\n", + " title - Display title for classification algorithm\n", + " labels - Actual values for target variable\n", + " predictions - Predicted values for target variable\n", + " \n", + " OUTPUT:\n", + " metrics - Dictionary of classification metrics for given title\n", + " \"\"\"\n", + " metrics = {\n", + " title: {\n", + " \"model\": title,\n", + " \"accuracy\": accuracy_score(labels, predictions),\n", + " \"precision\": precision_score(labels, predictions),\n", + " \"recall\": recall_score(labels, predictions),\n", + " \"f1-score\": f1_score(labels, predictions),\n", + " \"r2\": r2_score(labels, predictions)\n", + " }\n", + " }\n", + " print(metrics)\n", + " return metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 407, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time: 0.04841494560241699\n", + "{'Decision Trees': {'model': 'Decision Trees', 'accuracy': 0.8300878197785414, 'precision': 0.8640611724723875, 'recall': 0.7811059907834101, 'f1-score': 0.8204921339249698, 'r2': 0.32032898397069154}}\n", + "Accuracy on train set: 0.823510150622135\n" + ] + } + ], + "source": [ + "#DecisionTreeClassifier\n", + "start = time.time()\n", + "modelDC = DecisionTreeClassifier(max_depth = 12, min_samples_leaf = 10)\n", + "modelDC.fit(X_train, y_train)\n", + "end = time.time()\n", + "TimeDC = end - start\n", + "print('Time: ', TimeDC)\n", + "\n", + "#Evaluating model on test set\n", + "y_pred = modelDC.predict(X_test)\n", + "all_metrics.update(metrics_data(\"Decision Trees\", y_test, y_pred))\n", + "\n", + "#Evaluating model on train set\n", + "y_pred = modelDC.predict(X_train)\n", + "accuracyDC2 = accuracy_score(y_train, y_pred)\n", + "print('Accuracy on train set: {}'.format(accuracyDC2))" + ] + }, + { + "cell_type": "code", + "execution_count": 408, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Multinomial Naive Bayes': {'model': 'Multinomial Naive Bayes', 'accuracy': 0.8335242458953799, 'precision': 0.8315467075038285, 'recall': 0.8341013824884793, 'f1-score': 0.8328220858895706, 'r2': 0.3340751393510596}}\n", + "Accuracy on train set: 0.8366077275703995\n" + ] + } + ], + "source": [ + "#MultinomialNB\n", + "start = time.time()\n", + "modelNB = MultinomialNB(alpha=0.005)\n", + "modelNB.fit(X_train, y_train)\n", + "end = time.time()\n", + "TimeNB = end - start\n", + "\n", + "#Evaluating model on test set\n", + "y_pred = modelNB.predict(X_test)\n", + "all_metrics.update(metrics_data(\"Multinomial Naive Bayes\", y_test, y_pred))\n", + "\n", + "#Evaluating model on train set\n", + "y_pred = modelNB.predict(X_train)\n", + "accuracyNB2 = accuracy_score(y_train, y_pred)\n", + "print('Accuracy on train set: {}'.format(accuracyNB2))" + ] + }, + { + "cell_type": "code", + "execution_count": 409, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time: 0.021226167678833008\n", + "{'Gaussian Naive Bayes': {'model': 'Gaussian Naive Bayes', 'accuracy': 0.6380297823596792, 'precision': 0.5806745670009116, 'recall': 0.978494623655914, 'f1-score': 0.72883295194508, 'r2': -0.4479283667320999}}\n", + "Accuracy on train set: 0.64603798297315\n" + ] + } + ], + "source": [ + "#GaussianNB\n", + "start = time.time()\n", + "modelGNB = GaussianNB()\n", + "modelGNB.fit(X_train, y_train)\n", + "end = time.time()\n", + "TimeGNB = end - start\n", + "print('Time: ', TimeGNB)\n", + "\n", + "#Evaluating model on test set\n", + "y_pred = modelGNB.predict(X_test)\n", + "all_metrics.update(metrics_data(\"Gaussian Naive Bayes\", y_test, y_pred))\n", + "\n", + "#Evaluating model on train set\n", + "y_pred = modelGNB.predict(X_train)\n", + "accuracyGNB2 = accuracy_score(y_train, y_pred)\n", + "print('Accuracy on train set: {}'.format(accuracyGNB2))" + ] + }, + { + "cell_type": "code", + "execution_count": 410, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time: 0.11943674087524414\n", + "{'Logistic Regression': {'model': 'Logistic Regression', 'accuracy': 0.8518518518518519, 'precision': 0.8520801232665639, 'recall': 0.8494623655913979, 'f1-score': 0.8507692307692308, 'r2': 0.4073879680463558}}\n", + "Accuracy on train set: 0.8542894564505567\n" + ] + } + ], + "source": [ + "#Logistic Regression\n", + "start = time.time()\n", + "modelLR = LogisticRegression()\n", + "modelLR.fit(X_train, y_train)\n", + "end = time.time()\n", + "TimeLR = end - start\n", + "print('Time: ', TimeLR)\n", + "\n", + "#Evaluating model on test set\n", + "y_pred = modelLR.predict(X_test)\n", + "all_metrics.update(metrics_data(\"Logistic Regression\", y_test, y_pred))\n", + "\n", + "#Evaluating model on train set\n", + "y_pred = modelLR.predict(X_train)\n", + "accuracyLR2 = accuracy_score(y_train, y_pred)\n", + "print('Accuracy on train set: {}'.format(accuracyLR2))" + ] + }, + { + "cell_type": "code", + "execution_count": 411, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time: 1.4242494106292725\n", + "{'Random Forest': {'model': 'Random Forest', 'accuracy': 0.8331424207712868, 'precision': 0.8457234212629896, 'recall': 0.8125960061443932, 'f1-score': 0.8288288288288288, 'r2': 0.33254778875324087}}\n", + "Accuracy on train set: 0.9616895874263262\n" + ] + } + ], + "source": [ + "#RandomForestClassifier\n", + "start = time.time()\n", + "rfc = RandomForestClassifier()\n", + "rfc.fit(X_train, y_train)\n", + "end = time.time()\n", + "TimeRFC = end - start\n", + "print('Time: ', TimeRFC)\n", + "\n", + "#Evaluating model on test set\n", + "y_pred = rfc.predict(X_test)\n", + "all_metrics.update(metrics_data(\"Random Forest\", y_test, y_pred))\n", + "\n", + "#Evaluating model on train set\n", + "y_pred = rfc.predict(X_train)\n", + "accuracyRFC2 = accuracy_score(y_train, y_pred)\n", + "print('Accuracy on train set: {}'.format(accuracyRFC2))" + ] + }, + { + "cell_type": "code", + "execution_count": 412, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time: 0.03830265998840332\n", + "{'LinearSVC': {'model': 'LinearSVC', 'accuracy': 0.8518518518518519, 'precision': 0.8395245170876672, 'recall': 0.8678955453149002, 'f1-score': 0.8534743202416918, 'r2': 0.4073879680463558}}\n", + "Accuracy on train set: 0.8551080550098232\n" + ] + } + ], + "source": [ + "#LinearSVC\n", + "start = time.time()\n", + "svc = LinearSVC()\n", + "svc.fit(X_train, y_train) \n", + "end = time.time()\n", + "TimeSVC = end - start\n", + "print('Time: ', TimeSVC)\n", + "\n", + "#Evaluating model on test set\n", + "y_pred = svc.predict(X_test)\n", + "all_metrics.update(metrics_data(\"LinearSVC\", y_test, y_pred))\n", + "\n", + "#Evaluating model on train set\n", + "y_pred = svc.predict(X_train)\n", + "accuracySVC2 = accuracy_score(y_train, y_pred)\n", + "print('Accuracy on train set: {}'.format(accuracySVC2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model performance comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 413, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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modelaccuracyprecisionrecallf1-scorer2
0Decision Trees0.8300880.8640610.7811060.8204920.320329
1Multinomial Naive Bayes0.8335240.8315470.8341010.8328220.334075
2Gaussian Naive Bayes0.638030.5806750.9784950.728833-0.447928
3Logistic Regression0.8518520.852080.8494620.8507690.407388
4Random Forest0.8331420.8457230.8125960.8288290.332548
5LinearSVC0.8518520.8395250.8678960.8534740.407388
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" + ], + "text/plain": [ + " model accuracy precision recall f1-score r2\n", + "0 Decision Trees 0.830088 0.864061 0.781106 0.820492 0.320329\n", + "1 Multinomial Naive Bayes 0.833524 0.831547 0.834101 0.832822 0.334075\n", + "2 Gaussian Naive Bayes 0.63803 0.580675 0.978495 0.728833 -0.447928\n", + "3 Logistic Regression 0.851852 0.85208 0.849462 0.850769 0.407388\n", + "4 Random Forest 0.833142 0.845723 0.812596 0.828829 0.332548\n", + "5 LinearSVC 0.851852 0.839525 0.867896 0.853474 0.407388" + ] + }, + "execution_count": 413, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_metrics = pd.DataFrame(all_metrics).T\n", + "all_metrics = all_metrics.reset_index(drop=True)\n", + "all_metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 414, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ModelAccuracy_trainTime
0Decision Trees0.8235100.048415
1Multinomial Naive Bayes0.8366080.020381
2Gaussian Naive Bayes0.6460380.021226
3Logistic Regression0.8542890.119437
4Random Forest0.9616901.424249
5LinearSVC0.8551080.038303
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" + ], + "text/plain": [ + " Model Accuracy_train Time\n", + "0 Decision Trees 0.823510 0.048415\n", + "1 Multinomial Naive Bayes 0.836608 0.020381\n", + "2 Gaussian Naive Bayes 0.646038 0.021226\n", + "3 Logistic Regression 0.854289 0.119437\n", + "4 Random Forest 0.961690 1.424249\n", + "5 LinearSVC 0.855108 0.038303" + ] + }, + "execution_count": 414, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Creating new df to store model performances\n", + "Model = ['Decision Trees', 'Multinomial Naive Bayes', 'Gaussian Naive Bayes', 'Logistic Regression', 'Random Forest', 'LinearSVC']\n", + "Accuracy_train = [accuracyDC2, accuracyNB2, accuracyGNB2, accuracyLR2, accuracyRFC2, accuracySVC2]\n", + "Time = [TimeDC, TimeNB, TimeGNB, TimeLR, TimeRFC, TimeSVC]\n", + "\n", + "#Create df from lists\n", + "cols = ['Model', 'Accuracy_train', 'Time']\n", + "data = list(zip(Model, Accuracy_train, Time))\n", + "\n", + "performance = pd.DataFrame(data, columns=cols)\n", + "performance" + ] + }, + { + "cell_type": "code", + "execution_count": 415, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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modelaccuracyprecisionrecallf1-scorer2Accuracy_trainTime
0Decision Trees0.8300880.8640610.7811060.8204920.3203290.8235100.048415
1Multinomial Naive Bayes0.8335240.8315470.8341010.8328220.3340750.8366080.020381
2Gaussian Naive Bayes0.638030.5806750.9784950.728833-0.4479280.6460380.021226
3Logistic Regression0.8518520.852080.8494620.8507690.4073880.8542890.119437
4Random Forest0.8331420.8457230.8125960.8288290.3325480.9616901.424249
5LinearSVC0.8518520.8395250.8678960.8534740.4073880.8551080.038303
\n", + "
" + ], + "text/plain": [ + " model accuracy precision recall f1-score r2 \\\n", + "0 Decision Trees 0.830088 0.864061 0.781106 0.820492 0.320329 \n", + "1 Multinomial Naive Bayes 0.833524 0.831547 0.834101 0.832822 0.334075 \n", + "2 Gaussian Naive Bayes 0.63803 0.580675 0.978495 0.728833 -0.447928 \n", + "3 Logistic Regression 0.851852 0.85208 0.849462 0.850769 0.407388 \n", + "4 Random Forest 0.833142 0.845723 0.812596 0.828829 0.332548 \n", + "5 LinearSVC 0.851852 0.839525 0.867896 0.853474 0.407388 \n", + "\n", + " Accuracy_train Time \n", + "0 0.823510 0.048415 \n", + "1 0.836608 0.020381 \n", + "2 0.646038 0.021226 \n", + "3 0.854289 0.119437 \n", + "4 0.961690 1.424249 \n", + "5 0.855108 0.038303 " + ] + }, + "execution_count": 415, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Join result2018 with weather2018 to get the Maximum temperature (Degree C)\n", + "all_performance = pd.merge(left = all_metrics , right = performance ,\n", + " left_on = ['model'], right_on = ['Model'], how = 'left')\n", + "drop_cols = ['Model']\n", + "all_performance.drop(drop_cols, axis=1, inplace=True)\n", + "\n", + "all_performance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unfortunately, none of the models has good enough r2 values. The best model is Logistic Regression with $R^2$ just approximately 0.4. We cannot confidently say that Logistic Regression is a good fit to predict the salary of Data Scientists.\n", + "\n", + "**This question we leave for further exploration in future projects.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hamming Loss (HL) and Jacard Score On Models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Hamming loss is the fraction of labels that are incorrectly predicted ( evaluation metrics for a classifier model.) \n", + "- The Jaccard Index, also known as the Jaccard similarity coefficient, is a statistic used in understanding the similarities between sample sets. (To measure Similarity)" + ] + }, + { + "cell_type": "code", + "execution_count": 416, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import hamming_loss\n", + "from sklearn.metrics import jaccard_score\n", + "from sklearn.linear_model import SGDClassifier\n", + "from sklearn.multiclass import OneVsRestClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 417, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Clf: RandomForestClassifier\n", + "Jacard score: 0.7064343163538874\n", + "Hamming loss: 0.1672394043528064\n", + "---\n" + ] + } + ], + "source": [ + "def avg_jacard(y_true,y_pred):\n", + "\n", + " jacard = np.minimum(y_true,y_pred).sum(axis=0) / np.maximum(y_true,y_pred).sum(axis=0)\n", + " \n", + " return jacard.mean()\n", + "\n", + "def print_score(y_pred, clf):\n", + " print(\"Clf: \", clf.__class__.__name__)\n", + " print(\"Jacard score: {}\".format(avg_jacard(y_test, y_pred)))\n", + " print(\"Hamming loss: {}\".format(hamming_loss(y_pred, y_test)))\n", + " print(\"---\") \n", + "\n", + "rfc = RandomForestClassifier()\n", + "rfc.fit(X_train, y_train)\n", + "\n", + "y_pred = rfc.predict(X_test)\n", + "\n", + "print_score(y_pred, rfc)" + ] + }, + { + "cell_type": "code", + "execution_count": 418, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Clf: MLPClassifier\n", + "Jacard score: 0.7026332691072575\n", + "Hamming loss: 0.17678503245513555\n", + "---\n" + ] + } + ], + "source": [ + "mlpc = MLPClassifier()\n", + "mlpc.fit(X_train, y_train)\n", + "\n", + "y_pred = mlpc.predict(X_test)\n", + "\n", + "print_score(y_pred, mlpc)" + ] + }, + { + "cell_type": "code", + "execution_count": 419, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Clf: SGDClassifier\n", + "Jacard score: 0.7336919973100202\n", + "Hamming loss: 0.15120274914089346\n", + "---\n", + "Clf: LogisticRegression\n", + "Jacard score: 0.7402945113788487\n", + "Hamming loss: 0.14814814814814814\n", + "---\n", + "Clf: MultinomialNB\n", + "Jacard score: 0.7124183006535948\n", + "Hamming loss: 0.16800305460099274\n", + "---\n", + "Clf: LinearSVC\n", + "Jacard score: 0.7444005270092227\n", + "Hamming loss: 0.14814814814814814\n", + "---\n" + ] + } + ], + "source": [ + "sgd = SGDClassifier()\n", + "lr = LogisticRegression()\n", + "mn = MultinomialNB()\n", + "svc = LinearSVC()\n", + "\n", + "for classifier in [sgd, lr, mn, svc,]:\n", + " clf = OneVsRestClassifier(classifier)\n", + " clf.fit(X_train, y_train)\n", + " y_pred = clf.predict(X_test)\n", + " print_score(y_pred, classifier)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Findings: It has been found that better Hamming loss has been found in Logistic Regression and Linear SVC **which is 0.14815**
\n", + "Jaccard similarity scores gives us distribution of label sets when using the models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predicting what causing Job satisfaction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An examination of work satisfaction variables based on Stack Over Flow survey data from 2020. Job satisfaction can be defined by factors such as compensation, benefits, work environment, team members, work-life balance, education level, place, and so on. By analysing the Stack Over Flow survey data from 2020, I will try to find some features that are negatively and positively affecting job satisfaction in various countries." + ] + }, + { + "cell_type": "code", + "execution_count": 420, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Very satisfied 12439\n", + "Slightly satisfied 11953\n", + "Slightly dissatisfied 6269\n", + "Neither satisfied nor dissatisfied 4669\n", + "Very dissatisfied 3106\n", + "Name: CurrentJobSatis, dtype: int64" + ] + }, + "execution_count": 420, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['CurrentJobSatis'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 421, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Very satisfied', 'Slightly satisfied', 'Slightly dissatisfied', 'Neither satisfied nor dissatisfied', 'Very dissatisfied']\n", + "[12439, 11953, 6269, 4669, 3106]\n" + ] + } + ], + "source": [ + "participation_rate = df2020['CurrentJobSatis'].value_counts().keys().tolist()\n", + "print(participation_rate)\n", + "count = df2020['CurrentJobSatis'].value_counts().tolist()\n", + "print(count)" + ] + }, + { + "cell_type": "code", + "execution_count": 422, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.pie(count, explode = (0.01,0.01,0.1,0.1,0.2), labels = participation_rate, shadow=True, autopct=lambda p : f'{p:.2f}% ({p * sum(count)/100:,.0f})', textprops={'fontsize':18, 'weight':'bold'})\n", + "plt.title(\"Propotion of Job Satisfaction from the Dataset\",fontsize = 20)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**In further Analysis we will split into two categories like Satified or Not Satisfied**" + ] + }, + { + "cell_type": "code", + "execution_count": 423, + "metadata": {}, + "outputs": [], + "source": [ + "# Applying one hot encoding\n", + "df_indicator = df.isnull().astype(int).add_suffix('_nan')\n", + "df = pd.concat([df, df_indicator], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 424, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.model_selection import GridSearchCV" + ] + }, + { + "cell_type": "code", + "execution_count": 425, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "best mean cross-validation score: -0.262\n", + "best parameters: {'max_depth': 40, 'min_samples_leaf': 10}\n", + "test-set score: -0.259\n" + ] + } + ], + "source": [ + "# Grid search for good parameters, I used the mean absolute error as the main measure of quality\n", + "param_grid = {'min_samples_leaf': [10,15,20],'max_depth': [20,30,40]}\n", + "grid = GridSearchCV(RandomForestRegressor(n_estimators=100,n_jobs=-1, oob_score=True), param_grid=param_grid,\n", + " scoring='neg_mean_absolute_error',cv=5, return_train_score=True)\n", + "X_train_grit = X_train.sample(frac=0.5, random_state=42)\n", + "grid.fit(X_train_grit, y_train.loc[X_train_grit.index])\n", + "print(\"best mean cross-validation score: {:.3f}\".format(grid.best_score_))\n", + "print(\"best parameters: {}\".format(grid.best_params_))\n", + "print(\"test-set score: {:.3f}\".format(grid.score(X_test, y_test)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Here Random Forest is used to Predict the Job satisfaction, model did not yield much better output and turned out to be very complex to get insights.** Random forest Regressor, Logistic Regression which may yield good results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Trying with Logistic Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Used Sklearn library to create a Logistic Regression model.\n", + "Before creting model, need to create data, Using model coefficients, features that has negative and positive effect on job satisfaction to be calculated." + ] + }, + { + "cell_type": "code", + "execution_count": 426, + "metadata": {}, + "outputs": [], + "source": [ + "numericals = [\"Age\",\"SalaryUSD\",\"YearsCodePro\"]\n", + "categoricals = [\"Country\",\"EdLevel\",\"Employment\",\"Hobbyist\",\"UndergradMajor\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 427, + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "code", + "execution_count": 428, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Very satisfied 12439\n", + "Slightly satisfied 11953\n", + "Slightly dissatisfied 6269\n", + "Neither satisfied nor dissatisfied 4669\n", + "Very dissatisfied 3106\n", + "Name: CurrentJobSatis, dtype: int64" + ] + }, + "execution_count": 428, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2020['CurrentJobSatis'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Performing further Spliting of CurrentJobSatis Coloumn**\n", + "- Delete \"Neither satisfied nor dissatisfied\"\n", + "- Combine \"Very satisfied\" and \"Slightly satisfied\", label as \"Satisfied\" -->1\n", + "- Combine \"Very dissatisfied\" and \"Slightly dissatisfied\", label as \"Dissatisfied\"-->0\n", + "- Delete rows \"Neither satisfied nor dissatisfied\"" + ] + }, + { + "cell_type": "code", + "execution_count": 429, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "df = df2020.drop(df2020[df2020.CurrentJobSatis == \"Neither satisfied nor dissatisfied\"].index)\n", + "\n", + "df.CurrentJobSatis = [1 if each == \"Very satisfied\" else \n", + " 1 if each == \"Slightly satisfied\" else \n", + " 0 if each == \"Very dissatisfied\"else \n", + " 0 if each == \"Slightly dissatisfied\" else\n", + " each for each in df.CurrentJobSatis]" + ] + }, + { + "cell_type": "code", + "execution_count": 430, + "metadata": {}, + "outputs": [], + "source": [ + "# Dropping nan in Converted Salary if any\n", + "df = df.dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 431, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeSalaryUSDYearsCodeProCountryEdLevelEmploymentHobbyistUndergradMajorCurrentJobSatis
136116000.013.0United StatesBachelorsFull-timeYesComputer Science0
22232315.04.0United KingdomMastersFull-timeYesMath/Stat1
32340070.02.0United KingdomBachelorsFull-timeYesComputer Science0
44914268.07.0SpainNo DegreeFull-timeNoMath/Stat0
55338916.020.0NetherlandsNo DegreeFull-timeYesNo major1
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" + ], + "text/plain": [ + " Age SalaryUSD YearsCodePro Country EdLevel Employment \\\n", + "1 36 116000.0 13.0 United States Bachelors Full-time \n", + "2 22 32315.0 4.0 United Kingdom Masters Full-time \n", + "3 23 40070.0 2.0 United Kingdom Bachelors Full-time \n", + "4 49 14268.0 7.0 Spain No Degree Full-time \n", + "5 53 38916.0 20.0 Netherlands No Degree Full-time \n", + "\n", + " Hobbyist UndergradMajor CurrentJobSatis \n", + "1 Yes Computer Science 0 \n", + "2 Yes Math/Stat 1 \n", + "3 Yes Computer Science 0 \n", + "4 No Math/Stat 0 \n", + "5 Yes No major 1 " + ] + }, + "execution_count": 431, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols= [\"Age\",\"SalaryUSD\",\"YearsCodePro\", \"Country\",\"EdLevel\",\"Employment\",\"Hobbyist\",\"UndergradMajor\", \"CurrentJobSatis\"]\n", + "df = df[cols]\n", + "df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 432, + "metadata": {}, + "outputs": [], + "source": [ + "# one hot encoding\n", + "df = pd.get_dummies(df, columns = categoricals )" + ] + }, + { + "cell_type": "code", + "execution_count": 433, + "metadata": {}, + "outputs": [], + "source": [ + "# Normalization of numerical features\n", + "for each in numericals:\n", + " df[each] = (df[each] - df[each].min()) / (df[each].max() - df[each].min())" + ] + }, + { + "cell_type": "code", + "execution_count": 434, + "metadata": {}, + "outputs": [], + "source": [ + "#df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 435, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score" + ] + }, + { + "cell_type": "code", + "execution_count": 436, + "metadata": {}, + "outputs": [], + "source": [ + "# Split data into X and y\n", + "X = df.drop(\"CurrentJobSatis\", axis = 1)\n", + "y = df.CurrentJobSatis" + ] + }, + { + "cell_type": "code", + "execution_count": 437, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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33767 rows × 180 columns

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0 \n", + "... ... ... ... ... \n", + "41564 0 0 0 0 \n", + "41565 0 0 0 0 \n", + "41566 0 0 0 0 \n", + "41567 0 0 0 0 \n", + "41568 0 0 0 0 \n", + "\n", + " Country_Azerbaijan Country_Bahamas Country_Bahrain \\\n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + "5 0 0 0 \n", + "... ... ... ... \n", + "41564 0 0 0 \n", + "41565 0 0 0 \n", + "41566 0 0 0 \n", + "41567 0 0 0 \n", + "41568 0 0 0 \n", + "\n", + " Country_Bangladesh Country_Barbados Country_Belarus Country_Belgium \\\n", + "1 0 0 0 0 \n", + "2 0 0 0 0 \n", + "3 0 0 0 0 \n", + "4 0 0 0 0 \n", + "5 0 0 0 0 \n", + "... ... ... ... ... \n", + "41564 0 0 0 0 \n", + "41565 0 0 0 0 \n", + "41566 0 0 0 0 \n", + "41567 0 0 0 0 \n", + "41568 0 0 0 0 \n", + "\n", + " Country_Benin Country_Bhutan Country_Bolivia \\\n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + "5 0 0 0 \n", + "... ... ... ... \n", + "41564 0 0 0 \n", + "41565 0 0 0 \n", + "41566 0 0 0 \n", + "41567 0 0 0 \n", + "41568 0 0 0 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"41566 1 0 1 \n", + "41567 0 0 1 \n", + "41568 0 0 1 \n", + "\n", + " UndergradMajor_Arts and Science UndergradMajor_Business \\\n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "5 0 0 \n", + "... ... ... \n", + "41564 0 0 \n", + "41565 0 0 \n", + "41566 0 0 \n", + "41567 0 0 \n", + "41568 0 0 \n", + "\n", + " UndergradMajor_Computer Science UndergradMajor_Engineering \\\n", + "1 1 0 \n", + "2 0 0 \n", + "3 1 0 \n", + "4 0 0 \n", + "5 0 0 \n", + "... ... ... \n", + "41564 1 0 \n", + "41565 1 0 \n", + "41566 0 0 \n", + "41567 1 0 \n", + "41568 1 0 \n", + "\n", + " UndergradMajor_Info Systems UndergradMajor_Math/Stat \\\n", + "1 0 0 \n", + "2 0 1 \n", + "3 0 0 \n", + "4 0 1 \n", + "5 0 0 \n", + "... ... ... \n", + "41564 0 0 \n", + "41565 0 0 \n", + "41566 0 0 \n", + "41567 0 0 \n", + "41568 0 0 \n", + "\n", + " UndergradMajor_No major UndergradMajor_Other Science \\\n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "5 1 0 \n", + "... ... ... \n", + "41564 0 0 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data into train and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Checking Model Coefficent**" + ] + }, + { + "cell_type": "code", + "execution_count": 440, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 72.11%\n" + ] + } + ], + "source": [ + "# define the model\n", + "model = LogisticRegression()\n", + "# fit the model\n", + "model.fit(X, y)\n", + "\n", + "# get importance\n", + "importance = model.coef_[0]\n", + "\n", + "# make predictions for test data and evaluate\n", + "y_pred = model.predict(X_test)\n", + "predictions = [round(value) for value in y_pred]\n", + "accuracy = accuracy_score(y_test, predictions)\n", + "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have recieved **72% Accuracy**, which is good enough to move aheas with predictions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting Features affecting Job Satisfaction" + ] + }, + { + "cell_type": "code", + "execution_count": 445, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "results_df = pd.DataFrame()\n", + "results_df[\"Rates\"] = importance.tolist()\n", + "results_df[\"Columns\"] = X.columns\n", + "\n", + "new_index = results_df.Rates.sort_values(ascending = False).index\n", + "sorted_results = results_df.reindex(new_index)\n", + "filtered_results = sorted_results[np.abs(sorted_results.Rates) > 0.1]\n", + "\n", + "plt.figure(figsize =(20,30))\n", + "plt.barh(filtered_results.Columns, filtered_results.Rates)\n", + "plt.xlabel(\"Negative and Postive Features\", fontsize = 25)\n", + "plt.title(\"Features Affecting Job Satistaction\",fontsize = 25)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Top 2 features negatively effecting Job Satisfaction are age, country. So, in the elderly ages job satisfaction may decrease because of the personal expectation increases. In the same way, as the professional coding years increase, satisfaction may decrease. \n", + "\n", + "- Among the countries; most dissatisfied countries are Angolia, Rwanda, Krygyzstan, Sudan.\n", + "- UndergradMajor and other Science,are mostly satisfied.\n", + "- Most satisfied countries Malta, Ghana, Cyprus." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall, we performed various analyses on the Stack overflow developer survey and derived insights from it. We found which country has the highest no of respondents, which is the most popular language, education level of respondents, different roles of developers, and so on.
\n", + "Additionally, we performed machine learning models to predict the growth of languages, the salary of data scientists, what is causing job satisfaction. " + ] + } + ], + "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": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/readme.md b/readme.md index 26e9bb3..f182302 100644 --- a/readme.md +++ b/readme.md @@ -25,8 +25,18 @@ We welcome contributions from all levels of experience. If you think the communi ## Finding Insights from Stack Overflow Developer Survey + +1. To perform Analysis on 3 years Stackoverflow Dataset and get insights. +2. To perform Data Analysis and answer the below questions. + + Impact of higher education on salary of the surveyed developers. + + Impact of education/experience/responsibilities on gender inequalities. + + Impact on participation rate due to different ethnicity. + + To find whether there is any difference between men and women's income. + + Impact on the increase in popularity of a language in the current year due to developer’s interest in the previous year. + Stack Overflow is a professional community for developers, conducting a survey annually. Analyzing the dataset professionally using modern tools can enable us to answer real-world questions effectively. The dataset covers 275 questions in total. + ### Project Goals: 1. Perform Analysis on the last 3 years' Stack Overflow Dataset to extract insights.