{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stackoverflow 2019 Survey results analysis and insights extraction about OSS contributions\n",
    "This project follows the [CRISP-DM](https://www.datasciencecentral.com/profiles/blogs/crisp-dm-a-standard-methodology-to-ensure-a-good-outcome) process to answer the followig questions:\n",
    "   - How often do developers contribute to OSS?\n",
    "   - Do Hobyist developers contribute more often to OSS?\n",
    "   - Does OSS quality perception play a bias role towards OSS contribution?\n",
    "   - Are experienced developers contributing more frequently to OSS?\n",
    "   - Do developers contributing to the OSS have a higher income?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(88883, 85)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../data/so_survey_2019/survey_results_public.csv\")\n",
    "schema = pd.read_csv(\"../data/so_survey_2019/survey_results_schema.csv\")\n",
    "eu_countries = pd.read_csv(\"../data/listofeucountries.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column</th>\n",
       "      <th>QuestionText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Respondent</td>\n",
       "      <td>Randomized respondent ID number (not in order ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MainBranch</td>\n",
       "      <td>Which of the following options best describes ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hobbyist</td>\n",
       "      <td>Do you code as a hobby?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OpenSourcer</td>\n",
       "      <td>How often do you contribute to open source?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OpenSource</td>\n",
       "      <td>How do you feel about the quality of open sour...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Column                                       QuestionText\n",
       "0   Respondent  Randomized respondent ID number (not in order ...\n",
       "1   MainBranch  Which of the following options best describes ...\n",
       "2     Hobbyist                            Do you code as a hobby?\n",
       "3  OpenSourcer        How often do you contribute to open source?\n",
       "4   OpenSource  How do you feel about the quality of open sour..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "schema.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "      <th>MainBranch</th>\n",
       "      <th>Hobbyist</th>\n",
       "      <th>OpenSourcer</th>\n",
       "      <th>OpenSource</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Country</th>\n",
       "      <th>Student</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>UndergradMajor</th>\n",
       "      <th>...</th>\n",
       "      <th>WelcomeChange</th>\n",
       "      <th>SONewContent</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Sexuality</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>SurveyLength</th>\n",
       "      <th>SurveyEase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>I am a student who is learning to code</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Never</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Not employed, and not looking for work</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Primary/elementary school</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>Tech articles written by other developers;Indu...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>I am a student who is learning to code</td>\n",
       "      <td>No</td>\n",
       "      <td>Less than once per year</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Not employed, but looking for work</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>Tech articles written by other developers;Indu...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I am not primarily a developer, but I write co...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Never</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>No</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Web development or web design</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>Tech meetups or events in your area;Courses on...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>Tech articles written by other developers;Indu...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Once a month or more often</td>\n",
       "      <td>OSS is, on average, of HIGHER quality than pro...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>No</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>Tech meetups or events in your area;Courses on...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>White or of European descent;Multiracial</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent                                         MainBranch Hobbyist  \\\n",
       "0           1             I am a student who is learning to code      Yes   \n",
       "1           2             I am a student who is learning to code       No   \n",
       "2           3  I am not primarily a developer, but I write co...      Yes   \n",
       "3           4                     I am a developer by profession       No   \n",
       "4           5                     I am a developer by profession      Yes   \n",
       "\n",
       "                  OpenSourcer  \\\n",
       "0                       Never   \n",
       "1     Less than once per year   \n",
       "2                       Never   \n",
       "3                       Never   \n",
       "4  Once a month or more often   \n",
       "\n",
       "                                          OpenSource  \\\n",
       "0  The quality of OSS and closed source software ...   \n",
       "1  The quality of OSS and closed source software ...   \n",
       "2  The quality of OSS and closed source software ...   \n",
       "3  The quality of OSS and closed source software ...   \n",
       "4  OSS is, on average, of HIGHER quality than pro...   \n",
       "\n",
       "                               Employment                 Country  \\\n",
       "0  Not employed, and not looking for work          United Kingdom   \n",
       "1      Not employed, but looking for work  Bosnia and Herzegovina   \n",
       "2                      Employed full-time                Thailand   \n",
       "3                      Employed full-time           United States   \n",
       "4                      Employed full-time                 Ukraine   \n",
       "\n",
       "          Student                                            EdLevel  \\\n",
       "0              No                          Primary/elementary school   \n",
       "1  Yes, full-time  Secondary school (e.g. American high school, G...   \n",
       "2              No           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "3              No           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "4              No           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "\n",
       "                                      UndergradMajor  ...  \\\n",
       "0                                                NaN  ...   \n",
       "1                                                NaN  ...   \n",
       "2                      Web development or web design  ...   \n",
       "3  Computer science, computer engineering, or sof...  ...   \n",
       "4  Computer science, computer engineering, or sof...  ...   \n",
       "\n",
       "                             WelcomeChange  \\\n",
       "0  Just as welcome now as I felt last year   \n",
       "1  Just as welcome now as I felt last year   \n",
       "2  Just as welcome now as I felt last year   \n",
       "3  Just as welcome now as I felt last year   \n",
       "4  Just as welcome now as I felt last year   \n",
       "\n",
       "                                        SONewContent   Age Gender Trans  \\\n",
       "0  Tech articles written by other developers;Indu...  14.0    Man    No   \n",
       "1  Tech articles written by other developers;Indu...  19.0    Man    No   \n",
       "2  Tech meetups or events in your area;Courses on...  28.0    Man    No   \n",
       "3  Tech articles written by other developers;Indu...  22.0    Man    No   \n",
       "4  Tech meetups or events in your area;Courses on...  30.0    Man    No   \n",
       "\n",
       "                 Sexuality                                 Ethnicity  \\\n",
       "0  Straight / Heterosexual                                       NaN   \n",
       "1  Straight / Heterosexual                                       NaN   \n",
       "2  Straight / Heterosexual                                       NaN   \n",
       "3  Straight / Heterosexual              White or of European descent   \n",
       "4  Straight / Heterosexual  White or of European descent;Multiracial   \n",
       "\n",
       "  Dependents           SurveyLength                  SurveyEase  \n",
       "0         No  Appropriate in length  Neither easy nor difficult  \n",
       "1         No  Appropriate in length  Neither easy nor difficult  \n",
       "2        Yes  Appropriate in length  Neither easy nor difficult  \n",
       "3         No  Appropriate in length                        Easy  \n",
       "4         No  Appropriate in length                        Easy  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Respondent', 'MainBranch', 'Hobbyist', 'OpenSourcer', 'OpenSource',\n",
       "       'Employment', 'Country', 'Student', 'EdLevel', 'UndergradMajor',\n",
       "       'EduOther', 'OrgSize', 'DevType', 'YearsCode', 'Age1stCode',\n",
       "       'YearsCodePro', 'CareerSat', 'JobSat', 'MgrIdiot', 'MgrMoney',\n",
       "       'MgrWant', 'JobSeek', 'LastHireDate', 'LastInt', 'FizzBuzz',\n",
       "       'JobFactors', 'ResumeUpdate', 'CurrencySymbol', 'CurrencyDesc',\n",
       "       'CompTotal', 'CompFreq', 'ConvertedComp', 'WorkWeekHrs', 'WorkPlan',\n",
       "       'WorkChallenge', 'WorkRemote', 'WorkLoc', 'ImpSyn', 'CodeRev',\n",
       "       'CodeRevHrs', 'UnitTests', 'PurchaseHow', 'PurchaseWhat',\n",
       "       'LanguageWorkedWith', 'LanguageDesireNextYear', 'DatabaseWorkedWith',\n",
       "       'DatabaseDesireNextYear', 'PlatformWorkedWith',\n",
       "       'PlatformDesireNextYear', 'WebFrameWorkedWith',\n",
       "       'WebFrameDesireNextYear', 'MiscTechWorkedWith',\n",
       "       'MiscTechDesireNextYear', 'DevEnviron', 'OpSys', 'Containers',\n",
       "       'BlockchainOrg', 'BlockchainIs', 'BetterLife', 'ITperson', 'OffOn',\n",
       "       'SocialMedia', 'Extraversion', 'ScreenName', 'SOVisit1st',\n",
       "       'SOVisitFreq', 'SOVisitTo', 'SOFindAnswer', 'SOTimeSaved',\n",
       "       'SOHowMuchTime', 'SOAccount', 'SOPartFreq', 'SOJobs', 'EntTeams',\n",
       "       'SOComm', 'WelcomeChange', 'SONewContent', 'Age', 'Gender', 'Trans',\n",
       "       'Sexuality', 'Ethnicity', 'Dependents', 'SurveyLength', 'SurveyEase'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        Developer, desktop or enterprise applications;...\n",
       "2        Designer;Developer, back-end;Developer, front-...\n",
       "3                                    Developer, full-stack\n",
       "4        Academic researcher;Developer, desktop or ente...\n",
       "5        Data or business analyst;Data scientist or mac...\n",
       "                               ...                        \n",
       "88871                                              Student\n",
       "88872    Academic researcher;Designer;Developer, game o...\n",
       "88875                                  Academic researcher\n",
       "88876    Designer;Developer, front-end;Developer, full-...\n",
       "88877    Developer, back-end;Developer, desktop or ente...\n",
       "Name: DevType, Length: 81335, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DevType'].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the ratio of missing values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BlockchainOrg             45.80\n",
       "CodeRevHrs                43.98\n",
       "ConvertedComp             37.19\n",
       "CompTotal                 37.06\n",
       "MiscTechWorkedWith        32.96\n",
       "BlockchainIs              32.31\n",
       "PurchaseHow               31.25\n",
       "MgrMoney                  31.19\n",
       "MgrIdiot                  31.19\n",
       "MgrWant                   31.11\n",
       "PurchaseWhat              30.21\n",
       "UnitTests                 29.49\n",
       "WebFrameDesireNextYear    29.18\n",
       "CompFreq                  28.82\n",
       "WorkWeekHrs               27.43\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_mean = (df.isna().mean().round(4) * 100)\n",
    "missing_values_mean.sort_values(ascending=False)[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which columns have no missing values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "columns with no missing values:  {'OpenSourcer', 'Hobbyist', 'Respondent'}\n"
     ]
    }
   ],
   "source": [
    "print(\"columns with no missing values: \", set(df.columns[df.isnull().mean() == 0.0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Columns with less than 15% of the values are missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Age',\n",
       " 'Age1stCode',\n",
       " 'BetterLife',\n",
       " 'Containers',\n",
       " 'Country',\n",
       " 'DatabaseWorkedWith',\n",
       " 'Dependents',\n",
       " 'DevEnviron',\n",
       " 'DevType',\n",
       " 'EdLevel',\n",
       " 'EduOther',\n",
       " 'Employment',\n",
       " 'EntTeams',\n",
       " 'Ethnicity',\n",
       " 'Extraversion',\n",
       " 'Gender',\n",
       " 'Hobbyist',\n",
       " 'ITperson',\n",
       " 'JobFactors',\n",
       " 'JobSeek',\n",
       " 'LanguageDesireNextYear',\n",
       " 'LanguageWorkedWith',\n",
       " 'LastHireDate',\n",
       " 'MainBranch',\n",
       " 'OffOn',\n",
       " 'OpSys',\n",
       " 'OpenSource',\n",
       " 'OpenSourcer',\n",
       " 'PlatformDesireNextYear',\n",
       " 'PlatformWorkedWith',\n",
       " 'Respondent',\n",
       " 'ResumeUpdate',\n",
       " 'SOAccount',\n",
       " 'SOComm',\n",
       " 'SOFindAnswer',\n",
       " 'SOJobs',\n",
       " 'SOTimeSaved',\n",
       " 'SOVisit1st',\n",
       " 'SOVisitFreq',\n",
       " 'SOVisitTo',\n",
       " 'ScreenName',\n",
       " 'Sexuality',\n",
       " 'SocialMedia',\n",
       " 'Student',\n",
       " 'SurveyEase',\n",
       " 'SurveyLength',\n",
       " 'Trans',\n",
       " 'UndergradMajor',\n",
       " 'WelcomeChange',\n",
       " 'YearsCode'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(missing_values_mean[missing_values_mean < 15].sort_values(ascending=True).index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Respondents Countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total respondents in the USA 20949\n",
      "Total respondents in EU 24819\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "88751"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_vals = df.Country.value_counts()\n",
    "countries_vals_percentage = (countries_vals[:15] * 100 / df.Country.dropna().shape[0])\n",
    "# colors_list = ['#5cb85c','#5bc0de','#d9534f']\n",
    "# ax = countries_vals_percentage.plot(kind=\"bar\", color = colors_list)\n",
    "\n",
    "ax = countries_vals_percentage.plot(kind=\"bar\", figsize=(8,4),\n",
    "                                    width = 0.8, edgecolor=None, color=\"steelblue\")\n",
    "plt.ylabel(\"Respondents %\")\n",
    "plt.title(\"Respondents % by Country: Total {}\".format(df.Country.dropna().shape[0]));\n",
    "plt.xticks(fontsize=10)\n",
    "\n",
    "nb_respondents = list(countries_vals[:15])\n",
    "utils.display_values_above_bars(ax, nb_respondents)\n",
    "plt.show()\n",
    "df_eu_respondents = df[df.Country.isin(eu_countries.x)]\n",
    "df_eu_respondents.Country.unique()\n",
    "total_eu_respondents = df_eu_respondents.Country.shape[0]\n",
    "print(\"Total respondents in the USA\", countries_vals[\"United States\"])\n",
    "print(\"Total respondents in EU\", total_eu_respondents)\n",
    "# sanity check\n",
    "df.Country.value_counts().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of survey respondents from the EU coutries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_eu = (df_eu_respondents.Country.value_counts() * 100 / df_eu_respondents.Country.dropna().shape[0]).plot(\n",
    "    kind=\"bar\", figsize=(12,4),\n",
    "    width = 0.8, edgecolor=None);\n",
    "# plt.axhline(y=0.05,color='gray',linestyle='--')\n",
    "# plt.axhline(y=0.1,color='gray',linestyle='--')\n",
    "plt.ylabel(\"Respondents %\")\n",
    "plt.title(\"Respondents % from EU countries\");\n",
    "utils.display_values_above_bars(ax_eu, list(df_eu_respondents.Country.value_counts()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LanguageWorkedWith"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                HTML/CSS;Java;JavaScript;Python\n",
       "1                            C++;HTML/CSS;Python\n",
       "2                                       HTML/CSS\n",
       "3                            C;C++;C#;Python;SQL\n",
       "4    C++;HTML/CSS;Java;JavaScript;Python;SQL;VBA\n",
       "Name: LanguageWorkedWith, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.LanguageWorkedWith.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "languages = df['LanguageWorkedWith'].dropna().apply(lambda row: str(row).split(';')).explode().unique()\n",
    "\n",
    "languages_worked_with = df['LanguageWorkedWith'].dropna()\n",
    "language_responses = languages_worked_with.count()\n",
    "\n",
    "languages_perc = {}\n",
    "languages_count = {}\n",
    "for language in languages:\n",
    "    languages_perc[language] = df[\"LanguageWorkedWith\"].str.contains(\n",
    "        language, regex=False, na=False).sum() / languages_worked_with.shape[0]\n",
    "    languages_count[language] = df[\"LanguageWorkedWith\"].str.contains(\n",
    "        language, regex=False, na=False).sum()\n",
    "languages_perc = utils.sort_dict_by_vals(languages_perc)\n",
    "languages_count = utils.sort_dict_by_vals(languages_count)\n",
    "\n",
    "plt.title(\"Programming, Scripting, and Markup Languages: {} Respondents\".format(df[\"LanguageWorkedWith\"].dropna().shape[0]))\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel(\"Repondents %\")\n",
    "plt.bar(languages_perc.keys(), languages_perc.values(), width=0.8, edgecolor=None);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developer Type: Which of the following describe you?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        Developer, desktop or enterprise applications;...\n",
       "2        Designer;Developer, back-end;Developer, front-...\n",
       "3                                    Developer, full-stack\n",
       "4        Academic researcher;Developer, desktop or ente...\n",
       "5        Data or business analyst;Data scientist or mac...\n",
       "                               ...                        \n",
       "88871                                              Student\n",
       "88872    Academic researcher;Designer;Developer, game o...\n",
       "88875                                  Academic researcher\n",
       "88876    Designer;Developer, front-end;Developer, full-...\n",
       "88877    Developer, back-end;Developer, desktop or ente...\n",
       "Name: DevType, Length: 81335, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "devtypes = df['DevType'].dropna()\n",
    "devtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DevTypes list ['Developer, desktop or enterprise applications' 'Developer, front-end'\n",
      " 'Designer' 'Developer, back-end' 'Developer, full-stack'\n",
      " 'Academic researcher' 'Developer, mobile' 'Data or business analyst'\n",
      " 'Data scientist or machine learning specialist' 'Database administrator'\n",
      " 'Engineer, data' 'Engineer, site reliability' 'Developer, QA or test'\n",
      " 'DevOps specialist' 'Developer, game or graphics' 'Educator' 'Student'\n",
      " 'Engineering manager' 'Senior executive/VP' 'System administrator'\n",
      " 'Developer, embedded applications or devices' 'Product manager'\n",
      " 'Scientist' 'Marketing or sales professional']\n"
     ]
    }
   ],
   "source": [
    "devtypes_list = devtypes.apply(lambda row: str(row).split(';')).explode().unique()\n",
    "print(\"DevTypes list\", devtypes_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        Developer, desktop or enterprise applications;...\n",
       "2        Designer;Developer, back-end;Developer, front-...\n",
       "3                                    Developer, full-stack\n",
       "4        Academic researcher;Developer, desktop or ente...\n",
       "5        Data or business analyst;Data scientist or mac...\n",
       "                               ...                        \n",
       "88871                                              Student\n",
       "88872    Academic researcher;Designer;Developer, game o...\n",
       "88875                                  Academic researcher\n",
       "88876    Designer;Developer, front-end;Developer, full-...\n",
       "88877    Developer, back-end;Developer, desktop or ente...\n",
       "Name: DevType, Length: 81335, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_na_devtypes = df['DevType'].dropna()\n",
    "not_na_devtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DevTypes list ['Developer, desktop or enterprise applications' 'Developer, front-end'\n",
      " 'Designer' 'Developer, back-end' 'Developer, full-stack'\n",
      " 'Academic researcher' 'Developer, mobile' 'Data or business analyst'\n",
      " 'Data scientist or machine learning specialist' 'Database administrator'\n",
      " 'Engineer, data' 'Engineer, site reliability' 'Developer, QA or test'\n",
      " 'DevOps specialist' 'Developer, game or graphics' 'Educator' 'Student'\n",
      " 'Engineering manager' 'Senior executive/VP' 'System administrator'\n",
      " 'Developer, embedded applications or devices' 'Product manager'\n",
      " 'Scientist' 'Marketing or sales professional']\n"
     ]
    }
   ],
   "source": [
    "devtypes_list = not_na_devtypes.apply(lambda row: str(row).split(';')).explode().unique()\n",
    "print(\"DevTypes list\", devtypes_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of respondents:  81335\n",
      "\n",
      "Dev Types: {'Marketing or sales professional': 975, 'Senior executive/VP': 2107, 'Scientist': 3617, 'Product manager': 4032, 'Engineering manager': 4202, 'Educator': 4492, 'Academic researcher': 5930, 'Data or business analyst': 6294, 'Data scientist or machine learning specialist': 6460, 'DevOps specialist': 8862, 'System administrator': 8929, 'Designer': 9182, 'Database administrator': 9520, 'Engineer': 10975, 'Student': 11921, 'Developer': 72491}\n"
     ]
    }
   ],
   "source": [
    "# Get the developer type without the environment (after the comma e.g. Developer, desktop or enterprise applications)\n",
    "devtypes = set(dev.split(',')[0].strip() for dev in devtypes_list)\n",
    "devtype_count, devtype_perc = utils.count_substr_in_column(df, \"DevType\", devtypes, False)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel(\"Developers %\")\n",
    "plt.title(\"% of Respondents by Developer Type\")\n",
    "y_pos = np.arange(len(devtype_perc))\n",
    "ax_devs = plt.barh(y_pos, devtype_perc.values());\n",
    "plt.yticks(y_pos, devtype_perc.keys())\n",
    "plt.show()\n",
    "print(\"\\nNumber of respondents: \", df[\"DevType\"].dropna().shape[0])\n",
    "print(\"\\nDev Types:\", devtype_count)\n",
    "top8_devtypes = list(devtype_perc.keys())[:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developers perception of the OSS quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "os_perception = (df.OpenSource.value_counts() * 100 / df.OpenSource.shape[0]).sort_values()\n",
    "os_perception.plot(kind=\"barh\");\n",
    "plt.title(\"Devleopers perception of the OSS quality\")\n",
    "plt.xlabel(\"Developers %\")\n",
    "plt.yticks(rotation=0, size=8)\n",
    "plt.get(\"Developers %\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OpenSource contribution: \n",
    "The column OpenSource doesn't contain any missing value. All Respondents answered the corresponding question:\n",
    "How often do you contribute to open source?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Evaluating results and sharing insights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='red'>Question 1</font>\n",
    " <font color='red'> How often do developers contribute to OpenSource Software?  </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_opnesourcers = df.OpenSourcer.count()\n",
    "opensourcers_count = df.OpenSourcer.value_counts()\n",
    "colors = ['#ff9999','cornflowerblue','#ffcc99', '#99ff99']\n",
    "utils.plot_and_show_pie(opensourcers_count.index, opensourcers_count.values,\n",
    "                        \"Contribution Frequency to OSS: {} respondents\".format(df.Respondent.count()), 3, colors=colors);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Never                                                  36.334282\n",
       "Less than once per year                                28.095361\n",
       "Less than once a month\\nbut more than once per year    23.132658\n",
       "Once a month or more often                             12.437699\n",
       "Name: OpenSourcer, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.OpenSourcer.replace({\n",
    "    \"Less than once a month but more than once per year\": \"Less than once a month\\nbut more than once per year\"},\n",
    "    inplace=True)\n",
    "df.OpenSourcer.value_counts() * 100 / df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_list = ['lightgray', 'lightsteelblue', 'mediumseagreen', 'steelblue', 'cornflowerblue', 'cornflowerblue']\n",
    "ax_oss_contributors = (df.OpenSourcer.value_counts() / df.shape[0]).plot(\n",
    "    kind=\"bar\",\n",
    "    edgecolor=None, figsize=(6,4), color=colors_list);\n",
    "plt.ylabel(\"Respondents %\")\n",
    "plt.xticks(rotation=30, ha=\"right\")\n",
    "ax_oss_contributors.set_xticklabels(list(df.OpenSourcer.unique()))\n",
    "plt.title(\"Percentage of respondents contributing to OSS:\\n total {}\".format(df.OpenSourcer.value_counts().sum()));\n",
    "utils.display_values_above_bars(ax_eu, list(df.OpenSourcer.value_counts()))\n",
    "ax_oss_contributors.get_xticklabels()[2].set_color(\"green\")\n",
    "ax_oss_contributors.get_xticklabels()[0].set_color(\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='red'>Question 2 </font>\n",
    " <font color='red'> Do Hobbyist developers contribute more often to Open Source projects?</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yes', 'No'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Hobbyist.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hobbyist\n",
       "No      9564\n",
       "Yes    22731\n",
       "Name: Respondent, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.OpenSourcer == \"Never\"].groupby(\"Hobbyist\")[\"Respondent\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenSourcer</th>\n",
       "      <th>Hobbyist</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Less than once a month\\nbut more than once per year</th>\n",
       "      <th>No</th>\n",
       "      <td>2346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>18215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Less than once per year</th>\n",
       "      <th>No</th>\n",
       "      <td>4586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>20386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Never</th>\n",
       "      <th>No</th>\n",
       "      <td>9564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>22731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Once a month or more often</th>\n",
       "      <th>No</th>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>9925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             Respondent\n",
       "OpenSourcer                                        Hobbyist            \n",
       "Less than once a month\\nbut more than once per ... No              2346\n",
       "                                                   Yes            18215\n",
       "Less than once per year                            No              4586\n",
       "                                                   Yes            20386\n",
       "Never                                              No              9564\n",
       "                                                   Yes            22731\n",
       "Once a month or more often                         No              1130\n",
       "                                                   Yes             9925"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hobbyist_opensourcer = df.filter([\"Respondent\", \"OpenSourcer\", \"Hobbyist\"], axis=1).groupby(['OpenSourcer', 'Hobbyist']).count()\n",
    "hobbyist_opensourcer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OpenSourcer</th>\n",
       "      <th>Hobbyist</th>\n",
       "      <th>Respondent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Less than once a month\\nbut more than once per...</td>\n",
       "      <td>No</td>\n",
       "      <td>2346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Less than once a month\\nbut more than once per...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Less than once per year</td>\n",
       "      <td>No</td>\n",
       "      <td>4586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Less than once per year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>20386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Never</td>\n",
       "      <td>No</td>\n",
       "      <td>9564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Never</td>\n",
       "      <td>Yes</td>\n",
       "      <td>22731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Once a month or more often</td>\n",
       "      <td>No</td>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Once a month or more often</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         OpenSourcer Hobbyist  Respondent\n",
       "0  Less than once a month\\nbut more than once per...       No        2346\n",
       "1  Less than once a month\\nbut more than once per...      Yes       18215\n",
       "2                            Less than once per year       No        4586\n",
       "3                            Less than once per year      Yes       20386\n",
       "4                                              Never       No        9564\n",
       "5                                              Never      Yes       22731\n",
       "6                         Once a month or more often       No        1130\n",
       "7                         Once a month or more often      Yes        9925"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hobbyist_opensourcer = hobbyist_opensourcer.reset_index()\n",
    "hobbyist_opensourcer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hobbyist_opensourcer['Hobbyist'].replace({\"No\": \"Don't code as a Hobby\",\n",
    "                                \"Yes\": \"Code as a Hobby\"}, inplace=True)\n",
    "splot = utils.plot_grouped_bars(hobbyist_opensourcer, \"OpenSourcer\", \"Hobbyist\", \"Respondent\",\n",
    "                  title=\"Do Hobbyist contribute more to OSS?\",\n",
    "                        xlabel=\"\", ylabel=\"# Respondents\");\n",
    "splot.legend(loc='center left', bbox_to_anchor=(1.00, 0.5), ncol=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='red'>Question 3</font>\n",
    "<font color='red'>Does OSS quality perception play a bias role towards OSS contribution</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenSourcer</th>\n",
       "      <th>OpenSource</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Less than once a month\\nbut more than once per year</th>\n",
       "      <th>OSS is, on average, of HIGHER quality than proprietary / closed source software</th>\n",
       "      <td>8355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSS is, on average, of LOWER quality than proprietary / closed source software</th>\n",
       "      <td>1505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The quality of OSS and closed source software is about the same</th>\n",
       "      <td>8136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Less than once per year</th>\n",
       "      <th>OSS is, on average, of HIGHER quality than proprietary / closed source software</th>\n",
       "      <td>8437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSS is, on average, of LOWER quality than proprietary / closed source software</th>\n",
       "      <td>1867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The quality of OSS and closed source software is about the same</th>\n",
       "      <td>9776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Never</th>\n",
       "      <th>OSS is, on average, of HIGHER quality than proprietary / closed source software</th>\n",
       "      <td>8259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSS is, on average, of LOWER quality than proprietary / closed source software</th>\n",
       "      <td>2542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The quality of OSS and closed source software is about the same</th>\n",
       "      <td>11115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Once a month or more often</th>\n",
       "      <th>OSS is, on average, of HIGHER quality than proprietary / closed source software</th>\n",
       "      <td>5272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSS is, on average, of LOWER quality than proprietary / closed source software</th>\n",
       "      <td>727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The quality of OSS and closed source software is about the same</th>\n",
       "      <td>3782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                       Respondent\n",
       "OpenSourcer                                        OpenSource                                                    \n",
       "Less than once a month\\nbut more than once per ... OSS is, on average, of HIGHER quality than prop...        8355\n",
       "                                                   OSS is, on average, of LOWER quality than propr...        1505\n",
       "                                                   The quality of OSS and closed source software i...        8136\n",
       "Less than once per year                            OSS is, on average, of HIGHER quality than prop...        8437\n",
       "                                                   OSS is, on average, of LOWER quality than propr...        1867\n",
       "                                                   The quality of OSS and closed source software i...        9776\n",
       "Never                                              OSS is, on average, of HIGHER quality than prop...        8259\n",
       "                                                   OSS is, on average, of LOWER quality than propr...        2542\n",
       "                                                   The quality of OSS and closed source software i...       11115\n",
       "Once a month or more often                         OSS is, on average, of HIGHER quality than prop...        5272\n",
       "                                                   OSS is, on average, of LOWER quality than propr...         727\n",
       "                                                   The quality of OSS and closed source software i...        3782"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_oss_quality_hobbyist = df[df.Hobbyist == 'Yes'].filter([\"Respondent\", \"OpenSourcer\", \"OpenSource\"], axis=1).groupby(['OpenSourcer', 'OpenSource']).count()\n",
    "group_oss_quality_hobbyist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenSourcer</th>\n",
       "      <th>OpenSource</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Less than once a month\\nbut more than once per year</th>\n",
       "      <th>OSS is, on average, of HIGHER quality than proprietary / closed source software</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSS is, on average, of LOWER quality than proprietary / closed source software</th>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The quality of OSS and closed source software is about the same</th>\n",
       "      <td>1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Less than once per year</th>\n",
       "      <th>OSS is, on average, of HIGHER quality than proprietary / closed source software</th>\n",
       "      <td>1692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSS is, on average, of LOWER quality than proprietary / closed source software</th>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The quality of OSS and closed source software is about the same</th>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Never</th>\n",
       "      <th>OSS is, on average, of HIGHER quality than proprietary / closed source software</th>\n",
       "      <td>3026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSS is, on average, of LOWER quality than proprietary / closed source software</th>\n",
       "      <td>1217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The quality of OSS and closed source software is about the same</th>\n",
       "      <td>4931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Once a month or more often</th>\n",
       "      <th>OSS is, on average, of HIGHER quality than proprietary / closed source software</th>\n",
       "      <td>515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OSS is, on average, of LOWER quality than proprietary / closed source software</th>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The quality of OSS and closed source software is about the same</th>\n",
       "      <td>442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                       Respondent\n",
       "OpenSourcer                                        OpenSource                                                    \n",
       "Less than once a month\\nbut more than once per ... OSS is, on average, of HIGHER quality than prop...        1000\n",
       "                                                   OSS is, on average, of LOWER quality than propr...         252\n",
       "                                                   The quality of OSS and closed source software i...        1045\n",
       "Less than once per year                            OSS is, on average, of HIGHER quality than prop...        1692\n",
       "                                                   OSS is, on average, of LOWER quality than propr...         500\n",
       "                                                   The quality of OSS and closed source software i...        2300\n",
       "Never                                              OSS is, on average, of HIGHER quality than prop...        3026\n",
       "                                                   OSS is, on average, of LOWER quality than propr...        1217\n",
       "                                                   The quality of OSS and closed source software i...        4931\n",
       "Once a month or more often                         OSS is, on average, of HIGHER quality than prop...         515\n",
       "                                                   OSS is, on average, of LOWER quality than propr...         149\n",
       "                                                   The quality of OSS and closed source software i...         442"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_oss_quality_nothobbyist = df[df.Hobbyist == 'No'].filter([\"Respondent\", \"OpenSourcer\", \"OpenSource\"], axis=1).groupby(['OpenSourcer', 'OpenSource']).count()\n",
    "oss_quality_perception_groups = [group_oss_quality_hobbyist, group_oss_quality_nothobbyist]\n",
    "group_oss_quality_nothobbyist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_oss_quality_hobbyist = group_oss_quality_hobbyist.reset_index()\n",
    "group_oss_quality_nothobbyist = group_oss_quality_nothobbyist.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nr_rows = 1\n",
    "nr_cols = 2\n",
    "\n",
    "fig, axs = plt.subplots(nr_rows, nr_cols, figsize=(nr_cols*7,nr_rows*5), squeeze=False, sharex=False)\n",
    "\n",
    "\n",
    "splot1 = utils.plot_grouped_bars(group_oss_quality_hobbyist, \"OpenSourcer\", \"OpenSource\", \"Respondent\",\n",
    "                                 ylabel=\"# Respondents\", ax=axs[0][0], title=\"Hobbyists\", legend=False);\n",
    "splot2 = utils.plot_grouped_bars(group_oss_quality_nothobbyist, \"OpenSourcer\", \"OpenSource\", \"Respondent\",\n",
    "                                 ylabel=\"# Respondents\", ax=axs[0][1], title=\"not Hobbyists\", legend=False);\n",
    "\n",
    "plt.suptitle('Is the OSS quality perception biasing the contribution frequency?', size=14)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(-0.7, -0.5), ncol=1);\n",
    "plt.subplots_adjust(top=.86)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='red'>Question </font>\n",
    "<font color='red'>Does the number of years of experience influence the opensource contribution frequency?</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coding Years of experience including Education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, '1', 'Less than 1 year', '9', '3', '4', '10', '8', '2', '13',\n",
       "       '18', '5', '14', '22', '23', '19', '35', '20', '25', '7', '15',\n",
       "       '27', '6', '48', '12', '31', '11', '17', '16', '21', '29', '30',\n",
       "       '26', '33', '28', '37', '40', '34', '24', '39', '38', '36', '32',\n",
       "       '41', '45', '43', 'More than 50 years', '44', '42', '46', '49',\n",
       "       '50', '47'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the years of experience unique values\n",
    "df.YearsCodePro.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map string values of YearsCodePro to numbers to use later for years of experience groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  1.,  0.,  9.,  3.,  4., 10.,  8.,  2., 13., 18.,  5., 14.,\n",
       "       22., 23., 19., 35., 20., 25.,  7., 15., 27.,  6., 48., 12., 31.,\n",
       "       11., 17., 16., 21., 29., 30., 26., 33., 28., 37., 40., 34., 24.,\n",
       "       39., 38., 36., 32., 41., 45., 43., 51., 44., 42., 46., 49., 50.,\n",
       "       47.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_map = {'Less than 1 year': '0', 'More than 50 years': '51'}\n",
    "\n",
    "def mapper(val):\n",
    "    \"\"\"\n",
    "    Mapping a string value to an int\n",
    "    :param val(string) value to map\n",
    "    \"\"\"\n",
    "    return  int(value_map.get(val, val))\n",
    "\n",
    "df['YearsCodeProCleaned'] = df['YearsCodePro'].dropna().apply(mapper)\n",
    "df.YearsCodeProCleaned.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create years of experience ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = [0, 5, 10, 20, 40, 55]\n",
    "ranges = list(zip(tmp, tmp[1:]))\n",
    "ranges\n",
    "def in_the_range(ranges_):\n",
    "    \"\"\"\n",
    "    Map value to a range if it is inside it\n",
    "    \"\"\"\n",
    "    def f(x):\n",
    "        i = 0\n",
    "        while i < len(ranges_):\n",
    "            r = ranges[i]\n",
    "            if (x >= r[0]) and (x < r[1]):\n",
    "                if r[0] == 40:\n",
    "                    return  '>40'\n",
    "                else:\n",
    "                    return f'{r[0]} - {r[1]}'\n",
    "            i += 1\n",
    "        return f'>{r[1]}'\n",
    "    return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           NaN\n",
       "1           NaN\n",
       "2         0 - 5\n",
       "3         0 - 5\n",
       "4        5 - 10\n",
       "          ...  \n",
       "88878       NaN\n",
       "88879       NaN\n",
       "88880       NaN\n",
       "88881       NaN\n",
       "88882     0 - 5\n",
       "Name: Years of experience, Length: 88883, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Years of experience'] = (df['YearsCodeProCleaned'].dropna()).apply(in_the_range(ranges))\n",
    "df['Years of experience']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df['Years of experience'].value_counts()/ df[\"Years of experience\"].count()).plot(\n",
    "    kind=\"bar\", title=\"Coding Years of experience including Education\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group the respondents by Years of Experience ranges and OSS contribution frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Years of experience</th>\n",
       "      <th>OpenSourcer</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">0 - 5</th>\n",
       "      <th>Less than once a month\\nbut more than once per year</th>\n",
       "      <td>6428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Less than once per year</th>\n",
       "      <td>8329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never</th>\n",
       "      <td>12508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Once a month or more often</th>\n",
       "      <td>3239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">10 - 20</th>\n",
       "      <th>Less than once a month\\nbut more than once per year</th>\n",
       "      <td>4327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Less than once per year</th>\n",
       "      <td>5090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never</th>\n",
       "      <td>4660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Once a month or more often</th>\n",
       "      <td>2186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">20 - 40</th>\n",
       "      <th>Less than once a month\\nbut more than once per year</th>\n",
       "      <td>1749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Less than once per year</th>\n",
       "      <td>2247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never</th>\n",
       "      <td>2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Once a month or more often</th>\n",
       "      <td>1018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">5 - 10</th>\n",
       "      <th>Less than once a month\\nbut more than once per year</th>\n",
       "      <td>5331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Less than once per year</th>\n",
       "      <td>6045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never</th>\n",
       "      <td>6194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Once a month or more often</th>\n",
       "      <td>2454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">&gt;40</th>\n",
       "      <th>Less than once a month\\nbut more than once per year</th>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Less than once per year</th>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never</th>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Once a month or more often</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                        Respondent\n",
       "Years of experience OpenSourcer                                                   \n",
       "0 - 5               Less than once a month\\nbut more than once per ...        6428\n",
       "                    Less than once per year                                   8329\n",
       "                    Never                                                    12508\n",
       "                    Once a month or more often                                3239\n",
       "10 - 20             Less than once a month\\nbut more than once per ...        4327\n",
       "                    Less than once per year                                   5090\n",
       "                    Never                                                     4660\n",
       "                    Once a month or more often                                2186\n",
       "20 - 40             Less than once a month\\nbut more than once per ...        1749\n",
       "                    Less than once per year                                   2247\n",
       "                    Never                                                     2183\n",
       "                    Once a month or more often                                1018\n",
       "5 - 10              Less than once a month\\nbut more than once per ...        5331\n",
       "                    Less than once per year                                   6045\n",
       "                    Never                                                     6194\n",
       "                    Once a month or more often                                2454\n",
       ">40                 Less than once a month\\nbut more than once per ...          64\n",
       "                    Less than once per year                                    109\n",
       "                    Never                                                      122\n",
       "                    Once a month or more often                                  48"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.loc[df['YearsCodeProCleaned'] < 5, 'YearsCodeProCleaned'] = \"<5\"\n",
    "opensourcers_by_age = df.filter([\"Respondent\", \"OpenSourcer\", \"Years of experience\"], axis=1).\\\n",
    "    groupby([\"Years of experience\", \"OpenSourcer\"]).count()\n",
    "opensourcers_by_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "opensourcers_by_age = opensourcers_by_age.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "splot = utils.plot_grouped_bars(opensourcers_by_age, \"OpenSourcer\", \"Years of experience\", \"Respondent\",\n",
    "                        xlabel=\"\", ylabel=\"# Respondents\");\n",
    "plt.title(\"Frequency of contribution to OSS and years of experience groups\", size=13,\n",
    "          fontweight='light')\n",
    "splot.legend(loc='center left', bbox_to_anchor=(1.00, 0.5), ncol=1);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='red'>Question 5</font>\n",
    "<font color='red'>Do developers contributing to the OSS have a higher income? </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.594500e+04\n",
       "mean     5.519014e+11\n",
       "std      7.331926e+13\n",
       "min      0.000000e+00\n",
       "25%      2.000000e+04\n",
       "50%      6.200000e+04\n",
       "75%      1.200000e+05\n",
       "max      1.000000e+16\n",
       "Name: CompTotal, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarize the central tendency, dispersion and shape of the dataset’s distribution, excluding NaN values.\n",
    "df.CompTotal.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62000.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_salaries = np.median(df.CompTotal.dropna().values)\n",
    "median_salaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Respondent                -1.200000\n",
       "CompTotal              18551.717402\n",
       "ConvertedComp             26.195623\n",
       "WorkWeekHrs             8272.445241\n",
       "CodeRevHrs                70.322650\n",
       "Age                        3.127265\n",
       "YearsCodeProCleaned        3.309610\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Kurtosis to measure whether the distribution is too peaked.\n",
    "df.kurtosis(axis = 0, skipna = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outliers are massively skewing the data. Removing them to get a better interpretation of the remaining data\n",
    "sns.distplot(df.query('CompTotal<{}'.format(median_salaries*20)).CompTotal.dropna()) #, bins=10000);\n",
    "plt.xlabel(\"Salary\")\n",
    "plt.ylabel(\"Density\")\n",
    "plt.title(\"Developers salary distribution\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53533"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_data = df.filter([\"Respondent\", \"OpenSourcer\", \"Hobbyist\" , \"CompTotal\"], axis=1)\n",
    "salary_data = salary_data[salary_data.CompTotal < median_salaries * 20]\n",
    "salary_data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55945"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of Data points with salary outliers\n",
    "df.CompTotal.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53533"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of Salary Data points without outliers\n",
    "(df.CompTotal.dropna().values < median_salaries * 20).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OpenSourcer</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>mean_salary_formatted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Less than once a month\\nbut more than once per...</td>\n",
       "      <td>105824.852658</td>\n",
       "      <td>105,824.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Less than once per year</td>\n",
       "      <td>99803.203081</td>\n",
       "      <td>99,803.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Never</td>\n",
       "      <td>93806.636620</td>\n",
       "      <td>93,806.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Once a month or more often</td>\n",
       "      <td>115656.535993</td>\n",
       "      <td>115,656.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         OpenSourcer      CompTotal  \\\n",
       "0  Less than once a month\\nbut more than once per...  105824.852658   \n",
       "1                            Less than once per year   99803.203081   \n",
       "2                                              Never   93806.636620   \n",
       "3                         Once a month or more often  115656.535993   \n",
       "\n",
       "  mean_salary_formatted  \n",
       "0            105,824.85  \n",
       "1             99,803.20  \n",
       "2             93,806.64  \n",
       "3            115,656.54  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_data = salary_data.groupby(['OpenSourcer']).agg(CompTotal =(\"CompTotal\",'mean'))\n",
    "#  data.groupby(['OpenSourcer', 'Hobbyist']).agg(mean_salary =(\"CompTotal\",'mean'))\n",
    "# groupby('StationID', as_index=False)['BiasTemp'].mean()\n",
    "salary_data[\"mean_salary_formatted\"] = salary_data[\"CompTotal\"].apply(lambda x: '{:,.2f}'.format(x)).values.tolist()\n",
    "salary_data = salary_data.reset_index()\n",
    "# salary_data['Hobbyist'].replace({\"No\": \"Don't code as a Hobby\",\n",
    "#                                 \"Yes\": \"Code as a Hobby\"}, inplace=True)\n",
    "\n",
    "salary_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAFwCAYAAACPXAGtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdd7hU1dXH8e+PXqQoohFQsSD2ioo1vlasxNiNQoyGaCzRFGti74nR2GNHY+yNGHs3dqyIDRQFBAWkd4H1/rH2wGGYe7mUadz1eZ773DP7lNlnypp99tlFZkYIIYTya1DuDIQQQnARkEMIoUJEQA4hhAoRATmEECpEBOQQQqgQEZBDCKFCREBeDJIulHRD5vE+kkzSBpm0xyUdLamzpDGL8RybSjq4lvXHSjplEY7XVtKpeWkvSdpnUfMWIL3fy5XpubtIej/9/aLA+lUlPSDpK0mDJT0tacMS5k+SzpM0UNKHkj6R9PtSPf/SIOkvkj6W9LmkvpIaluJ5IyAvnpeAnTKPfwq8lUtLb972wItL8BybAjUGZDO70cyuXITjtQVOXehWFaZUX4RSkNRoKR3q58DrZraZmd2d9xyNgWeAN8xsTTNbG7gZeE7S8kvp+RfmQGAXYAsz2wTYDHhqaR18ab2OCznO+8AmwPrAOkDPpfGcC2Vm8beIf0BzYDqwcnr8NrAP8EB63A0YmpY7A2OAi9Kb/DmwfVrXCHga6A8MBG4HmgDtgKHAeOAD4OoCeTgX+Fta3hZ4L207EDiswPb/BWalbV5PaS8BfwX+B3wFXJrZfhXgwXRuA4Aza3gtNgJeTc//CXBySl8N+A5onNn2IaB3Wt4LeA14F3gD6J7Sd0p5vAZ4M72uh+M/eO+nv10yx9wh5e8j4B/AN8CGaV1X4EngHeBD4KgazqFnOsYHwMfATin9D2nf91MeN83sY8Byaflvmed4Hlg9770/N73GxwEjgVUyx7m60GsLLJc+Dx+nv9NS+i/S6zoq5XetvP16AW8WON59wF/S8h2kII1/Hm8GmqR1rYFb0vuee00bLuzzkvdcpwCPAqph/WmZ87o98zreAZyQ2W7u47R8DR7Y301p26S8fJj+dl/Y+57etz+lc7mAhXx3gIbp/d+nJLGlFE+yLP7hQegQoFV6IxsCg9K6PwJ903Ln9CHYJz3+BfBaWhbQLrN8J3BsevxL4MFanv9c5gXkx4AjM8dpW2D7zsCYvLSX0he1AdAGDx5d0rpngR3TcpN0vrsVOG4roGlaXg4Pyuulx88D+6Xldun4LYG18ADXOq3bgHk/YDsBs4FtMs/RjvTlTl+24Wm5KTAc2CE93j+91hviP3bvAutm8vl57nHeOXyYOUbDTL7aZ7bZlUygY/6AvGIm/Rjg3rz3/pDM+kuBc9JySzywrlQgT5cBfdP72Rr/jO2Z/94X2O9vwFUF0k/JfZ7w4PZRer8a4SXqXOC7JfNZagDcA/x6YZ+XvOdaJb3Wg/GAewTQKK3bEw/ErZn3mb8sk6/aAnJ/oGV6vAL+w7Rt5n1bfmHve3o/Tss8R63fHeBGvDDToBRxZWldQtVHL+LBYyLwPzObLWlQqkfeCS8N5kw2s8fT8pvAFWm5AfBHSXsy7wM1dTHzcoak1YFnzeytRdj3ATObA0yQ9CmwlqQR6RzaS8pt1wpYDw/UWS2AGyRtAswBOuCXep/iX6JfAv3wUu5jZjZF0h54UH4lc/xGklZOy4PM7I3Mc6wF3COpI/Aj8BNJPwFWAqaZ2asAZvaIpPFpn3VSfu/NPEfTlPZZ3jm8AFwh6QHgSTP7OKVvIelM/Ms/Jx2zkD0lHc+8AJc1Hbg/8/g64H+SLgKOBJ4xs1EFjrkr8DvzqDBR0j0p7cka8pCjhazPuc/MJgNI6gscAFwL7AdsJekPabsW+I9ezgKfF2BQ9sBmNjJ9D7bBq+7OwoNyj3QO95rZxPTcN+Gl8Lp40MympOVtgE/M7PX0nLOBcZLWZ+Hve9/MMWv87kjaO+V/q3TORRcBefG9hH+5JgAvp7RXgJ3xN/GEzLYzMsuzmfe6H5623cHMJqUvf01f+hqZ2VWS/oN/2K+R9IyZ/bmOu08vkLcGeEliSzP7cSH7X4yXVH5pZrMkPQM0S+seAq6U1A4PzCendAFPmVmv/INJWg+YnJd8D/AHM3tUUgP8R6tZOk5Ng7EIvyLYdCH5x8xOkbQR/t49IOnv+Jf2Qfwq4T1JHYBvC+R3deBK/LUaImlb4N+ZTaakoJp7rmGS3sGrSX4L/KaW/OefW10GnvkwHTdfd7xUvLDnEvAzM/uqhm0LfV4WYGaz8KuqVyXdBnwnaQVqP69ZzH9fq1nedtnPRU0/PHV53+ceZyHfnc3wH8zFKSQtlript/hexy9JD8CDM3hAPhEYb2Zf1+EYbfEPzyRJbfAAnTMRvyxcKEnrmNmXZvZPvLSxVYHNJgIt6nJDxMwm4V+m0zPPsWoqlRY6h2EpGG+I1+nmjjMVvyS8GK8GeDWtegbokdcqZctastQWGJKWj8ZLPOAlnpaStkvH6Jm2Bb9MnSrpyMxzrCupdf7BJXU1swFm9g/gX8CWeDBoBAxLmxUKcuCX3jPxgNMAOLaW88i5BrgKmJV3JZD1LHBMarHQCjgUr/NdmHuB5bOtGiQdiF/xXJvZ7iBJLdPn4Qjm3YDuB5yeu5kqaUVJa9TheeeStIWkzpmkzYFx+D2RZ4FDJbWSF2GPyZzXl/hrj6RVgP+r5WleB9aXtE3avmG6aVnn9z2tq+278yhepVIyUUJeTGY2XdJbQEczG5GS3wE6Ag/U8TB3Aj0lDcRLX6/iNwzB61//KOlD4GUzO6mW45wk6f/wwDAD/1HIz+9YSXcDAySNM7NtF5K3X+Cl2wHp8STgV3hpOOtC4C5JR+BfqFfy1t+ezusvmbwMStvfKqk5Xkf9Gv76FXIy8Kikb/GrkR/ScWZIOhy4UdJUvOrhe2BC+oHYF7hK0p/wKqHvKdxy5VJJXfAS2njgaDObKOls4B1JQ6mhqsDMBqSqjoH4jdiXgR1rOI/cPi9Lmg5cX8tmF+ABNPf632VmC22pYGYzJe0O/F3SiXhVy1d4/f/YzKav4AFntbR8U0o/Gbgc+FCS4Z+nk5n3g1gXKwLXpyA4A7+i+Vm67H9S0sb4PQTweuEL0/JNwIPpM/8FfiO3pvMcK+nn6TxbpvP8o5k9twjvO9T+3dke/z5+sAjnvkRyN0pCqEqSWqUSPemL1RfoXKo6v8WRSpyvAWuX8nI48/x3AP3N7NqFbRtKK0rIododIO8g0wCv3zyswoPx+fiVxh/KEYxDZYsScgghVIi4qRdCCBUiAnIIIVSIogRkSbdJGiXp40zaXyV9JukjSY9IaptZd4Z8EJTPU6eBXHqPlDZYUrYJ1hqS3pJ3xLhPUpOU3jQ9HpzWdy7G+YUQQjEUpQ5Z0o544+s7zWzDlLY78EJqjnQZgJmdJu9Zcw/e/q8D3iYx1zniC2A3vKfQO/gNm08k3Q88bGb3SroR+NDMbpD0W2BjMztW0qHA/mZ2yMLyu+KKK1rnzp2X3gsQQgjAu+++O8bM2td1+6K0sjCzV/JLp2b2TObhm/iIUOA9lu41sxnAEEmDmdc4e3Cux5Cke/E2u5/iPapynSj64n37b0jHOjelPwhcK0m2kF+dzp07079//0U8yxBCqJ2kbxZl+3LVIf+KeQ3tOzKvNxR4abhjLent8J5ws/LS5ztWWj8hbb8ASX0k9ZfUf/To0Ut8QiGEsKRKHpAlnYX3iMqN41qoT7otRnptx1ow0ewmM+tmZt3at6/zFUUIIRRNSTuGSOqNj2+7S6YaYTiwamazTkCuK3Kh9DFAW0mNUik4u33uWMNTH/02QLa7aAghVKySlZAl9cAHpt4vr4dSP3ywkaapS2kXfHDsd4AuqUVFE3xwlX4pkL/IvDro3vgANrlj9U7LB+I3EaPnSwihKhSlhCwfu3UnYEVJw4FzgDPwUbqe9UGeeNPMjjWzganVxCd4VcbxaWxTJJ2Az6jRELjNzAampzgNH+/0Qnw0/1tT+q34QDeD8ZLxocU4vxBCKIboOg1069bNopVFCGFpk/SumXWr6/bRUy+EECpEBOQQQqgQMfxmCKGoLnts2a4OPK1nnWskFipKyCGEUCEiIIcQQoWIgBxCCBUiAnIIIVSICMghhFAhIiCHEEKFiIAcQggVIgJyCKHsHrjmfC7ovTtXnjRvgp+PXnuOv594MGfsvxXDB38y3/Yjvx7E9af9ir+feDBXnnQoP86cAcClv96PKRPHAzB88Kdc1qcn3371eelOZAlFx5AQQtltsfM+bLvXwdz/j3Pmpv1ktbU48vTLefj6S+bbdvbsWdx35dkcfPJ5dFhjHaZMHE/DhvOHspFfD+Luy0/n8D9eTMc1u5bkHJaGCMghhLJbc4PNGfv9iPnSVlp1jYLbDnr/LX7SeW06rOFTb7Zs3Xa+9aOGDeGBq8/lkJPPY9V1NihOhoskAnIIoaqMGfENQtx67olMmTiOTbbfnZ/+vNfc9Xde8kcOOfl8Oq+/aRlzuXiiDjmEUFXmzJnN159+yKG/v4BjL7mFgW+9xOAP3567fu2Nt+Kd5x5lzuzZZczl4omAHEKoKm3arcwaG2xGy9ZtadK0GV0333a+G3c9+/wJgEf/eVm5srjYIiCHEKpKl8268903g5k5YzqzZ89iyMD3WDlT36wGDTjsDxcx+ttveObfN5Yxp4su6pBDCGV3zxVn8dXH7zJl4nguPnpvdju0D81btabfzX9jyoRx3HHBKayyxjocfe41tFiuNTvsdzjX/rEXkui6+Xas2237+Y7XqHETep95Bf88qw+t2q7ANnsdXKYzWzQxhRMxhVMIxVSfx0OOKZxCCKFKRZXFQmx/YJ9yZ6Go/vfgTeXOQgghiRJyCCFUiAjIIYRQISIghxBChYiAHEIIFSICcgghVIgIyCGEUCGKEpAl3SZplKSPM2krSHpW0qD0f/mULklXSxos6SNJm2f26Z22HySpdyZ9C0kD0j5XS1Jtz1Fsg/q/zNuP38X7zz44N+3HmdP5+NUnePep+/j41SeYlQbQBpgwegQfPPcQ7z3zAANe/s/c9DcevX3u8tiRQ3n3qfuYMXVyKU4hhFABilVCvgPokZd2OvC8mXUBnk+PAfYEuqS/PsAN4MEVOAfYGtgKOCcTYG9I2+b267GQ5yiqlVZfh/W323O+tG8//5C2K3Vgix6H0HalDgz//AMAZs2cwZfvv8Z62+7B5rsfRNetd13geONHfcuQD19n/e33pGmL5UpxCiGEClCUgGxmrwBj85J7An3Tcl/gZ5n0O829CbSVtAqwB/CsmY01s3HAs0CPtK61mb1h3u/7zrxjFXqOomrTfhUaNWk6X9oPI75hpdV8AO2VVluHH0Z8A8DoYV/SrmPnuYG2SbPm8+03YcxIBr/7Kutt24Pmy7UuQe5DCJWilD31VjazkQBmNlLSSim9IzAss93wlFZb+vAC6bU9xwIk9cFL2ay22mqLe041+nHGNJo0bwFAk+Yt+HHGNACmTZ6AzZnDgJcfZ/asH+mw9gastLoHbpszm89ef5YNf7o3LfJmQag0u/3phnJnoWie/etx5c5CqKcq4aaeCqTZYqQvEjO7ycy6mVm39u3bL+rui83mzGHy+DGsv90ebLD9ngz79H2mTfJJGdWgAa3arcT3Q6pnUsYQwtJTyoD8fapuIP0fldKHA6tmtusEjFhIeqcC6bU9R8k1btqcmdOmAjBz2lQaN/WqiaYtWrL8yp1o2KgxjZs2o3X7nzBlQq52R3TdelcmjxvNsM/eL1POQwjlUsqA3A/ItZToDTyWSe+VWlt0Byakaoengd0lLZ9u5u0OPJ3WTZLUPbWu6JV3rELPUXIrrLI6o4Z+AcCooV/QrsPqc9MnjvkOmzOH2bNmMXnsaJq3mlc90bBRI9bbbg9GDx3M90M+K0veQwjlUZQ6ZEn3ADsBK0oajreWuBS4X9LRwFDgoLT5E8BewGBgKnAUgJmNlXQB8E7a7nwzyxUlj8NbcjQHnkx/1PIcRfX5Wy8wYcwIZs2YzjtP/JvV1tucTl034fO3nuf7IZ/TtMVydO2+CwAtWi9P25U78f5zDyGJlTt3pWWbFeY7XuMmzdhg+z0Z8PJ/aNS0Ge06dC7FaYQQyqwoAdnMDqth1S4FtjXg+BqOcxtwW4H0/sCGBdJ/KPQcxdZ1650Lpm+4494F0zt13YROXTdZIH2bnx01d7lpi+XotmdNL2MIYVlUCTf1QgghEAE5hBAqRswYEopm4FN3MebLATRp0YptjvoLAD9Om8KAx29l2oQfaN6mHRvtewyNm7XAzPjihQcYM2QgDRs1Zv09e9F65dWYNuEHPnj4+rn7f/vR/xj+watsfvDvaNysRTlPL4SlLkrIoWg6bNCdzQ48Yb60r99+mhVW68p2x5zHCqt15eu3ngbghyEDmTpuFNsefS7r7f4LPnv23gWON3LgWwx77yU2O/DECMZhmRQBORTN8qt2oXGzlvOljR78Eats0B2AVTbozujBH2bSt0YSbTqswawZU5kxecLc/b7/7F2+fvsZNjvoRJrE+B5hGRVVFqGkZk6dRNPl2gDQdLk2zJw6CYAZk8fTrNW8wfmatlqeGZPH07j5ckyfOJbPnr+PrXudSdOWbcqS77rode0z5c5CUd15wu7lzsIyL0rIoSJ468d83ku+SYvlaNZ6Bb7//N3SZiqEEouAHEqqSYtWc6siZkyeQJMWrQBo1mp5pk8aN3e7GZPGzS1JN2jUhM0OOJ5vP3yVkZ+8XfpMh1AiEZBDSbVfa2NGDnwTgJED36T92hun9I0YOfAtzIwJI4bQqGnzuQEZPJBvdsAJfPlqP34Y8klZ8h5CsUUdciiaAY/fxrhhX/DjtMm8euOZrLnd3qy+9e4M+M+tfDvgdZq1XoGN9z0GgHZrbsiYIQN5/ZZzaNC4CRv0OHKB4zVvuyKb7H8sHzx8HRv37EObVdYo9SmFUFQRkEPRbLTPrwqmb3Hw7xZIk8S6ux66QHrzNu3mtkEGaLVSJ3Y49pKll8kQKkhUWYQQQoWIgBxCCBUiAnIIZfTpS4/Q7+I+9Lvo13z64sPzrRv4/APcdeIeTE+tUr588xnevv9awGeeee2uv/L63VfU0GQwVKMIyCGUybgRXzPo9SfZ649Xs8/pNzL847eYOOpbAKaMG8XIz96n5fILTgtpZrx539XMmT2LbQ47BZ+nISwLIiCHUCYTvx9K+87r0ahJMxo0bMjKXTZm2EevAdD/4X+yec+joUCwfeehG5g5ZSLbHXkqahBf4WVJvJshlEnbVTrz/eABzJgykVkzp/PtwHeYMm40wwa8QYs2K7JCp7UW2GfIuy8yduggdjjqTBo0bFiGXIdiimZvIZRJm5+sxga7Hcxz155Bo6bNWL7jGqhBQwY8fQ+7Hl+4ad8KndZm4qjhjPnmc1Zac4MS5zgUW5SQQyijLtv0YO/TrmOPk6+gaYtWLNduZSb/8B2PX3ocD5/Ti6njR/Pfy49n2kSfTrLNyquyw1Fn8urtFzN+5NdlzXtY+iIgh1BG0yaNB2DK2FEM/fA11txqVw6+5H5+ft6d/Py8O2nRtj17n3odzVvPmwh3pTU3YOtDTuSFG89mythR5cp6KIKosgihjF655XxmTJ1EgwYN2ergE2iaBltamE4bdmf65Ak8f8NZXrpu2brIOQ2lEAE5hDLa45S/17r+5+fdOXd5re67s1b3eWMSr919D9buvkfR8hZKL6osQgihQkRADiGEChEBOYQQKkQE5BBCqBAlD8iSTpE0UNLHku6R1EzSGpLekjRI0n2SmqRtm6bHg9P6zpnjnJHSP5e0Rya9R0obLOn0Up9fCCEsrpIGZEkdgZOAbma2IdAQOBS4DLjSzLoA44Cj0y5HA+PMbG3gyrQdktZP+20A9ACul9RQUkPgOmBPYH3gsLRtCCFUvHJUWTQCmktqBLQARgI7Aw+m9X2Bn6Xlnukxaf0u8qGtegL3mtkMMxsCDAa2Sn+DzewrM5sJ3Ju2DSGEilfSgGxm3wJ/A4bigXgC8C4w3sxmpc2GAx3TckdgWNp3Vtq+XTY9b5+a0kMIoeKVuspiebzEugbQAWiJVy/ky424XWigV1uM9EJ56SOpv6T+o0ePXljWQwih6EpdZbErMMTMRpvZj8DDwLZA21SFAdAJGJGWhwOrAqT1bYCx2fS8fWpKX4CZ3WRm3cysW/v27ZfGuYUQwhIpdUAeCnSX1CLVBe8CfAK8CByYtukNPJaW+6XHpPUvmM9X0w84NLXCWAPoArwNvAN0Sa02muA3/vqV4LxCCGGJlXQsCzN7S9KDwHvALOB94Cbgv8C9ki5MabemXW4F7pI0GC8ZH5qOM1DS/XgwnwUcb2azASSdADyNt+C4zcwGlur8QghhSZR8cCEzOwc4Jy/5K7yFRP6204GDajjORcBFBdKfAJ5Y8pyGEEJpRU+9EEKoEBGQQwihQkRADiGEChEBOYQQKkQE5BBCqBARkEMIoUIsNCCn4TI7lCIzIYRQn9WlHfJXwOWS2gD/AR40s7HFzVYIIdQ/Cy0hm9ljZnYE3oV5V+BrSQ9I2r7ouQshhHpkoSVkSXsBhwDL4yXkPvioao8BOxY1dyGEUI/UpcpiA+AsMxueTZT06+JkKYQQ6qe6tLLYJT8YA5jZ50XITwgh1Ft1KSGPkHQaPrPHHAAze6GouQohhHqoLgH5G6AZsF16bEAE5BBCWMoWGpDN7LxSZCSEEOq7unQMOULSq5J+kPSVpP6lyFgIIdQ3dbmpdxKwEzAAWAf4uJgZCiGE+qouAXlqmh7pR3ym6E2Lm6UQQqif6hKQL5HUDDgfuBb4R3GzFEII9VONN/Uk5YL1s+n/a+kvhBBCEdTWyuJ5vIkbeFfpHAN2LlqOQgihnqoxIJvZ/5UyIyGEUN/VZXChTYA/AauQSspmFiXkEEJYyupyU+9G4Jy0bR/gjaLmKIQQ6qm6BOQZZvYl0MDMBgM7FDlPIYRQL9UlID+Vmr39W9IHwKdFzlMIIdRLdRnL4tK0+M/0F0IIoQhqLCFLOjvNo4ek/ST1l/S6pIOW5AkltZX0oKTPJH0qaRtJK0h6VtKg9H/5tK0kXS1psKSPJG2eOU7vtP0gSb0z6VtIGpD2uVqSCuUjhBAqTW1VFrua2YS0fDmwG/BT4HdL+Jz/AJ4ys3WBTfAqkNOB582sC97++fS07Z5Al/TXB7gBQNIK+I3GrYGtgHNyQTxt0yezX48lzG8IIZREbQG5EYCkjYFvzGycmf1IGqR+cUhqjc/DdyuAmc00s/FAT6Bv2qwv8LO03BO409ybQFtJqwB7AM+a2VgzG4f3JuyR1rU2szfMzIA7M8cKIYSKVlsd8kOSXgRWJpWKJXUAZi7B860JjAZuT+2b303HXtnMRgKY2UhJK6XtOwLDMvsPT2m1pQ8vkB5CCBWvxhKymV0B7Ad0M7PceBZjgQOW4PkaAZsDN5jZZsAU5lVPFFKo/tcWI33BA0t9Ur14/9GjR9ee6xBCKIFam72Z2SQzm5p5PD1Tr7w4hgPDzeyt9PhBPEB/n6obSP9HZbZfNbN/J2DEQtI7FUhfgJndZGbdzKxb+/btl+CUQghh6ahLO+Slxsy+A4ZJ6pqSdgE+AfoBuZYSvYHH0nI/oFdqbdEdmJCqNp4Gdpe0fLqZtzvwdFo3SVL31LqiV+ZYIYRQ0eoyySmSNgLaMm8si1eW4DlPBO6W1AT4CjgK/2G4X9LRwFAg17TuCWAvYDAwNW2LmY2VdAHwTtrufDMbm5aPA+4AmgNPpr8QQqh4dRlc6HHgW+Zd+huw2AHZzD4AuhVYtUuBbQ04vobj3AbcViC9P7Dh4uYvhBDKpS4lZJnZb4qekxBCqOfqEpAnSLoSn9zUYG7pNIQQwlJUl4D8dNFzEUIIoU6DC/WV1BRYicLtfEMIISwFdbmpdzrerGxd4EtgBrBrkfMVQgj1Tl3aIe+Xpmz6wsx2AH4ocp5CCKFeqktAzo1dMVXSjsD6RcxPCCHUW3UJyL9Ldch/AA4E/ljcLIUQQv1Ul1YWH+M959oDvwc2K2qOQgihnqpLCflfeLfpw8xsFnBJcbMUQgj1U10CcnszuxGYXuzMhBBCfVaXgDxK0iFAc0n7AyOLnKcQQqiX6hKQf4XXH7+Hjy/866LmKIQQ6qkab+pJWi3zsF/6A++xN7SYmQohhPqotlYWX+PjDQ/MpAkfYOhXRcxTCCHUS7UF5K3xGZs3AD4FHkpjDYcQQiiCGgOymb1DmpFD0tbAzZL6m1nUIYcQQhHUVoe8ItATn8ljOnARPqVSCCGEIqitymIk8D7wIj6excbAxpIws7NLkbkQQqhPagvIMcRmCCGUUG11yC+XMiMhhFDf1aVjSAghhBKIgBxCCBUiAnIIIVSICMghhFAhIiCHEEKFiIAcQggVoiwBWVJDSe9Lejw9XkPSW5IGSbpPUpOU3jQ9HpzWd84c44yU/rmkPTLpPVLaYEmnl/rcQghhcZWrhPw7fMCinMuAK82sCzAOODqlHw2MM7O1gSvTdkhaHzgUH/ioB3B9CvINgeuAPfHZsQ9L24YQQsUreUCW1AnYG7glPRawM/Bg2qQvPsoc+FgafdPyg8AuafuewL1mNsPMhgCDga3S32Az+8rMZgL3pm1DCKHilaOEfBVwKjAnPW4HjE8TqAIMBzqm5Y7AMIC0fkLafm563j41pYcQQsUraUCWtA8wyszezSYX2NQWsm5R0wvlpY+k/pL6jx49upZchxBCaZS6hLwdsJ+kr/HqhJ3xEnNbSblxNToBI9LycGBVgLS+DTA2m563T03pCzCzm8ysm5l1a9++/ZKfWQghLKGSBmQzO8PMOplZZ/ym3Atm9gt8iM8D02a9gcfScr/0mLT+BTOzlH5oaoWxBtAFeBsfUL9LarXRJD1Hbi7AEEKoaLUNv1lKpwH3SroQH4P51pR+K3CXpMF4yfhQADMbKOl+4BNgFnC8mc0GkHQC8DTQELjNzAYSQghVoGwB2cxeAl5Ky1/hLSTyt5kOHFTD/hfhs5jkp9ky25UAACAASURBVD9BzGwSQqhC0VMvhBAqRATkEEKoEBGQQwihQkRADiGEChEBOYQQKkQE5BBCqBARkEMIoUJEQA4hhAoRATmEECpEBOQQQqgQEZBDCKFCREAOIYQKEQE5hBAqRATkEEKoEBGQQwihQkRADiGEChEBOYQQKkQE5BBCqBARkEMIoUJEQA4hhAoRATmEECpEBOQQQqgQEZBDCKFCREAOIYQKEQE5hBAqRATkEEKoEBGQQwihQpQ0IEtaVdKLkj6VNFDS71L6CpKelTQo/V8+pUvS1ZIGS/pI0uaZY/VO2w+S1DuTvoWkAWmfqyWplOcYQgiLq9Ql5FnAH8xsPaA7cLyk9YHTgefNrAvwfHoMsCfQJf31AW4AD+DAOcDWwFbAObkgnrbpk9mvRwnOK4QQllhJA7KZjTSz99LyJOBToCPQE+ibNusL/Cwt9wTuNPcm0FbSKsAewLNmNtbMxgHPAj3SutZm9oaZGXBn5lghhFDRylaHLKkzsBnwFrCymY0ED9rASmmzjsCwzG7DU1pt6cMLpBd6/j6S+kvqP3r06CU9nRBCWGJlCciSlgMeAk42s4m1bVogzRYjfcFEs5vMrJuZdWvfvv3CshxCCEVX8oAsqTEejO82s4dT8vepuoH0f1RKHw6smtm9EzBiIemdCqSHEELFK3UrCwG3Ap+a2d8zq/oBuZYSvYHHMum9UmuL7sCEVKXxNLC7pOXTzbzdgafTukmSuqfn6pU5VgghVLRGJX6+7YAjgQGSPkhpZwKXAvdLOhoYChyU1j0B7AUMBqYCRwGY2VhJFwDvpO3ON7Oxafk44A6gOfBk+gshhIpX0oBsZv+jcD0vwC4Ftjfg+BqOdRtwW4H0/sCGS5DNEEIoi+ipF0IIFSICcgghVIgIyCGEUCEiIIcQQoWIgBxCCBUiAnIIIVSICMghhFAhIiCHEEKFiIAcQggVIgJyCCFUiAjIIYRQISIghxBChYiAHEIIFSICcgghVIgIyCGEUCEiIIcQQoWIgBxCCBUiAnIIIVSICMghhFAhIiCHEEKFiIAcQggVIgJyCCFUiAjIIYRQISIghxBChYiAHEIIFSICcgghVIgIyCGEUCGWyYAsqYekzyUNlnR6ufMTQgh1scwFZEkNgeuAPYH1gcMkrV/eXIUQwsItcwEZ2AoYbGZfmdlM4F6gZ5nzFEIICyUzK3celipJBwI9zOyY9PhIYGszOyFvuz5An/SwK/B5STNasxWBMeXORAWK12VB8ZoUVkmvy+pm1r6uGzcqZk7KRAXSFvjVMbObgJuKn51FI6m/mXUrdz4qTbwuC4rXpLBqfl2WxSqL4cCqmcedgBFlyksIIdTZshiQ3wG6SFpDUhPgUKBfmfMUQggLtcxVWZjZLEknAE8DDYHbzGxgmbO1KCquGqVCxOuyoHhNCqva12WZu6kXQgjValmssgghhKoUATmEECpEBOQQMiQVajYZQklEQA4hkSRLN1UkrVnu/CwKSQ0yy/GjUia590HScpJap5ZedRYBOYQkE4x7AUeXOTt1ln5I5qTldS3u1JeFpIZmNkdSI7yp7bnAXyV1rusxIiCHkCFpH+Bw4Pb0uOKbhmZ+SE4BflPm7NRbZjY7LV4JPGRmvwfeB86UtGNdjhEBOdR72ct94E1gFHA8zG3XXvHfE0m7ATsAj5U7L/VNropIrgGwOrAfgJndATwJ7JdGoqxVxX/QQigmSQ3SZeZakg4FuptZL2CapH9Jap2rDqg0mUDQFHgd+BTYRdJPypqx+mtdoKOZ7Qc8L+khSaua2SPAnzMl6BpFQA71WgrGzYG7gcbAkZIuBc4G+gNHlDN/NUk/JCZpE+Bq4FfAg8CPwBmSNi5rBuuR9D7sA9wK/ELSc2Z2Od5b+CFJywMz6nKsCMihVrnLLEntJa1Y7vwUyc/xSQ1eAtoDbYBngVvM7Poy5qtG6YekJXAz3lX4p8DhZnY+8AGwTjnzV59IagbsARwEDABGpx/Mm4B9zWxcXW+0VvwNi1BeZjZbUlvgr/hd40oZZ3ax5TVva2Jmd6flS4E/AFOA3wIzy5fLwrJ5B+bgEzBMAJYDLpfUG3jKzEaWK4/1QWoW+b2ZTQGaAMOAa4EmZra3pPMkfW1mty/KcSMgL+PyvsCL6zdAezP7eikes2wywfi3wNqSDLgUmIb/8DQDDjGzmakp00Lr/kolk/e9gY+ALsBzwCFAd+AwM+tbvhwu+yRtALQCzpK0GV69dQOwEdBc0onAxmZ2zqIeOwLysq8zMARA0knALOB9M3ujph0KBNz+wLppFL3bzGxqEfNbEpK2Ao4EdsRvhr1hZuel1grfmdm36XWomGAMc1uENAR2AbqZ2XGSrkiPNwd+WcbsLfMkbQj808y2S/XGmwONzGwSfv9hLzxYH7JYx6/igk5YiHQX/k5gEl6KOgz4F3ASXj96z0L2/1na90NgNWAbYCXgOjMbVcSsF0WuRUVaXg/YFfgW+D/gVOAM4LJ0GVqRJHUwsxFp+SzgHTN7Jt2YnGNmdbp5FBZP+iE/A7/HsCpwPz4V3Id4k8lGZtZ/cY8fN/WWYeaOBCYCpwF/M7PH8CqIoySdXdO+ko4CjgF+BpyHl8pewwPYInUHrRTpRlgLSUcDXwN74ZeapwAXAg0qMRhnuuM2Bq6T9HtJZ+JXPl0AzGxaBOPiM7O3gY+Bi4H3zOx9vLprHbylTtMlOX6UkJdRknbA60L74w3V98Ybqx9rZu9LagFsbmb/y9tPQAvgKvwG1zH4rCsfAA8Ab5vZhJKdyFKQVzJeDr9qeAy4C7gF+AJY1cyOT9tUTB15pp10e2ANoCVe170G/t5sBhxjZg+XMZvLvOy9BElb4leKJwEPmNktKb2LmQ1aouepkM9dWMpSwH0U2B6fhfsVST8HfofXgf07b/uGqUVF7v9ywPJp270k3QfcbWZVOx2WpPXM7FNJ7fCbdten9CZmNjMtzw3e5SapmZlNT8uP4DfxWgEfm9ltklYDfpJKbaFIMj+KbfHqiu+A9/D34xZgmJmdvDR+yOOm3jIm9+Exs6mS7sKbcPWS9JaZPSxpEl4XnN1HKQgL6CvpFaAt3qTqdUkPA99UazBO57UqcJOkB/Bp4teQNNPMbskFY/BqjXLlMyt17DhS0tV464nPgX/gnQ2ulbQqMMLMhpYxm/VC5jNxJfAN8AoemB/H78v8Nm23xKXbqENehqTAmrs03w0Ybmb749UND0v6C9AmdR6YK/NB+jvwLvAlfsOrIT5p7N1mdko6btV9ZlJd+lCgJ/AM8AmwAnCApB5QeUNWmtlHeJvvk4GRwFD8B/Jc/MrlSvz9CSWQets1BO4zs3fxILymmc00s6uW2vNElcWyR9KxwMHAi3jgOQfYCh/F7FQzK9i5Q9KpeE+jX+PBeQzQxcz+k9ZXVJvc2mQuM9fAv0hfm9mszPqOwNZAWzO7rVz5zJd6Qx4GPGpmw1I10z74/YCpwAt4N+lTzey98uW0/knvxY7AQ/hn6iJg96V5I7jqSjuhdvJh/k4E9jKzC/DS8Q3ACPzmzwLBWNJ2kn6FN905Hg/Ew4C+eGkZmG94wYqXgnEb/NybmI/aplxJ2My+xVspHClphXLmNc8OeOeUe1K98QT8zn2DtPwlcE4E49LJXBU+BzyF34f5DXD60m6VE3XIy4C8G1GvAV/hdcG9zKyvpAlAq2z9aN4NiAn4zaJX8LEc2gEXAOeb2SeV1OpgER2Ad4TJBWHLq5oYC5xoZmPLkbkaPAbsC6yPN2mbif+o7opf9TxuZi+WL3vLvszV1U+A6fjAQNPMbCLwlKTn8Sur0Uv9uavzexZy8j482+Ftjp8DjsW/2CeY2Vc17NsSWC21PLgB+MDM/pnWtTOzH0pzFktH/g+HpM3xaonGwGup7q/iyQd02hwfMGgycHO66bpmTe9lWLokrYQP3PQ58AM+4PzgYhdOIiAvA9Il1bPAW3iJagO8nvEwYEUzu7iG/Q7GR6n6GL/Z9Rf8MuzraqovzifpNLxVxYd4CXkO0Al41cxeKWfe6iqV5Lvg7cdXAq7J9dALxZENtpJuxasnpgGn452IpmbvQxRD1CEvG7oBI83sTDM7F3ger0O+KRuMC7QkeNLMjsY7G+wCbIh3Nqiq+uIsSXvizcQuxX+QhuI/NkPw0k5VSC1DvsA743zAMjDKXiXLC8Yd8aqj2cBx+PyKu1OC6bEiIFehAoH1A2CSfJQp8EHKt83fJ/OB2y2VqqcCmNmFZnY1XgrYSNJ8+1aZH/D2ob3xJnwv492/nzGz78uZscVhZsPx3mAVNxTosiTz3TgcH6BpNt4T7x2gAx6MHyx2PuKmXhXKfHiOwAdTH4r3GNo13ZlvDfyxhn32xpvuvJAtBacqiqGSPgHWxqcEqniZOvSWeNXED8DG+NgCv8fHqB1aU1O/alApnVWWdZK2x6sndjOz7yXNBDbB782cXoof9KhDrjKZANQZuA+/8bA83pKgLz5QucxscIF9O+Btky9OrS8a4iOEZW+E7QC8Xg1VFpnXogE+it0cfPr1FfH21zPxu+Fnpu2rtbVIKBLNP85JQ3y28Zb4uNIlHw87AnIVyQSgVfHuz13N7AL5mAb74QPOnJea5+T2yR+jYg/gEuAvZvbfspzIUibp9/iVwk14tcssvEfV1Mw2FTNGRagMme9TB/zmNvjAU33w79MJZvZljQcogqhDrhK5btHyIRivwOuIt5N0OjAcLyE+lBeMc2NULIcP25jr4nkgcLKkM0p8GktdGvNhM7yt6BgzuxnoAWyZ1ufaIEcwDvNJ3yfhV5rt8ZY5j+J1xQ+wmIPML4koIVcZSf/Ap1M6PPUwOwGfFeR35rMWFNrnVryqYgJwvZmtmrroHmNml5Yo60UjaRd8kPkRwPf4OANHW5pyKoSapK7155jZL9Pj3nhT0SvKkZ8oIVefvsBWkk5JPcwuxHvYFRycXFJuAsav8HGND5K0M7BlLhirigYMKtDCBDN7Hm+m1AHvDHNjBONQSP7nx8yGADPl02CBj464bqHPWSlECbmKZOqB2+FDMU4ys+Nq2b4xXp+6PX4Zdh8+08E9wFHVFrTyzr810Di11c2tXw3/0ZkJPG9mA8qU1VDh5DPidMSvqB7F2xrvhA8adHq5enVWTcmoPirwaz5bPpj6D2Z2BNBA0qZ5++Sm+9kP+DcefFsBu+E3vs7EW1l8XU0lY5h7/s3wHlQ9gAck7ZRZn2v+1wDvQh7CXJnvxmrAUfhsOu2APwPX4z/mvcrZxT5KyBUqUxrsjPc8G2VmL6R1Ta2W+dMkdcEHB/oz/ot/ffp7BGhqZtOqtQmYpF74mM7XSHoD7403y8yGV+s5heLLtKhYDR8jZEszOymt+zXeRf1YM/uunPmsqhJSfZJpHdEPHwv3Rkl/SutmpDaTNdkOH0O3oZl9jpcGNjafSWRaOkZVBK7sVYKkRnj3550lvYr3qFodOFVSo2o5p1B6KRg3wgsqqwObSroxFXxuBs4udzCGCMiVbhfgMnyq8R+AQyXdAbWPNWFmd+AzKfeVdABwEN4So+pkehgeCfzWzN7Cp1tvgQ+i9DfgumIP+hKWCZfgtQIXmtmOwGjgZUkrms/QUnZRZVHB5AOsT8ODzj/xuuAzgf1t3gy4+UNOZmfH/T+8TvVLM9s9f321SL0HLwf2NbMxqeXIJsBawCAzezc6foSFkbQJcBveaejylPYLvP3+9LJmLomAXAUknY/PGvF/wM/TgDP522wGfI3Xp07KBWr5uK7X4+M5/L6U+V4S2R+aFJC7m9lf0+OGwEZm9kE58xiqT2q7fw0+lOavy52ffFFlUSEyd4CbpbrjrH/ig/38soZgfCBwHnAq0CddglkqNY7CZ5qompHOUr5NUiP5oEFfAVtI2kxSa/wLtUN5cxkqWeb7tIOkY9JyIzMba2a/AOakQkxFiRJyBciUZrsBpwHfAqPN7KKFXYrLZ8O9H+/meTbeQeQMoIH5PHJVVUWRVzK+EVgFvxGzKj6M5ihgZTPrVb5chmqQWlQcBzxiZm+ntIr+PkQJuQKkYNwUH/rvHHzmj20lNS4UjHMtLFKwHoe3xDgCWMXMTsOHnNwhHbtiP3yFZILx0fj0RX/Gu4dPBc4wsz/hrSuqqodhKI28tvt7Mm+wqdz3ZXa5euHVRXygyygvoMzCuzhvC/wi/R0qad+8fZQJshfK59KbibfHvUfSX/DScdVOhJnGF9gR+CH1tjsbvwI4GOYNFBQ38UJWrqorLbcxnx/yDOBySVtmPjcVWy0QVRZlkndp3gfvvrkF3qLifrxp1++AvQt9gOQjtzU0sxPT45/h7ZVXzdz8qpqWB5mG++2A8fhobXsCA4Cn8VlQflJt3b1D6Um6OS1uiLfBb4KPAXO6mT1dtozVQQTkMpN0KrC+mf0yNVzvjndzbgFcZWbfFthnObwZ2A258RpSSXl0pslbRdeVZWXq0DcFzsJ7F94MGH7FMB4/12llzGaoAvKZxs83s31Ss8/L8fGNJwMTrcKn8YopnMokVVc0BtYHrgRIN+HeM7P/pTvCszLb54JWYzObLOkx4DhJt+MTYP4Lv6T/Nh2rKoIxzK1Db4wPfHQz8AXQC3gfuA5YN4JxqKNJwHRJrc3sRUnnAlub2Y1lzledRB1yiWXqjRuaj0fxGT7/G6lJ1/WSNsgLxg1T0NoGeFDSBfhAQffhHUUuA64tVJquFmb2Iz5b9udmNhD4Kz6Q/kwze7msmQtVw8wGAW8A/STtht8kn1LeXNVdlJBLKFNP2h44S9JzwH+BCyStBeyMTz46MLtfujO8Mj7z7XV4KeBI4FUz219SOzP7obRnUxSjgYvkg/B3x6vUxpU5T6HKmNkVkgbjV5//MLO7y52nuoo65DKQ9E+8V936wDP4L3ozoLmZvZPZLnvj7yB8AsYtzexTSV3x+tZ7zOzJEp9C0aRWJV3wKXWuNbNvq+nmZCgNSWtZmu9O0rH4JA1jrQIGCFoSEZBLLA2S0wOfon4cXuXQBrg0e8OhUBCStCcehP9qZo+lpj0TSpf7pSNTH94db+43yXxUukLbVs3NyVAaqSXOn4BPgK7ARsA7wHf4VeNnZczeEomAXGKS1sarG74DHjWzkfLZCx4ws8kFtr8En9K+CV5CHoyPa3yTmd2atqmacYAz1Tbr4+fzKF6Hfn+upF9N5xNKK3XqaIg3Ed0OL9zsD7QEegJt8Wq/sg0yvyQiIBdZpjS4H97Z4Q08GG+P1+E/Ymaf1LBvd7yn2kH4ZfwFeEP3UUBLM/umBKew1KXxKZ7Bm/U9IGlbvPfdx2Z2dVkzFypWCsYbAt8AG+OFlN3wxglX403bfg48ZWYjy5XPJRGtLIosBePNgIuAJ/F2tZviwwAubJqhOcBsPPh+hE/JtLGZjTGzbyq5C2i+bK9EM5uCvxbnStrczF4HrsKrcEKoSUtgJXxc46vwwPxXfNKCE4C1zOz2ag3GEAG5VGbil+TP4yOybQLsYGbX1FQ6BkgDovwXb5GxC/Ar/JIst74qLm/SVcKctNxT0tpmdiFwEt6t9XAz+8TM7sptX878hsqTqrom481E18N7cK5sPvP6/fj8eG1rOURViGZvpTEZ2FjSPmb2uKSZ+C/9XPk38TI3sx7Bx7jYD3i8Whq4Z2VaivwZ7xL9Zupt+A98NK4LJD1saZDwavmhCaWR991oiXca2gjYKjUh3Rx4w9Kck9Us6pCXsppuSEnaEi8Rfgt0NbP9C+2Tmn29ln75s/s3yJQyq64ZWOoW/RczO0DSQ/jYFKPxsZ4HpqqduJkXapTGb9kCr+56GVgZH551a1tGhmONgFwk8pk65pjZmExaC3xKpoZWYA44SWcDW+NjOPwNeKXaAm9t5MOG9sRnPjkH72F4g5m9V9aMhYonaX/gUDM7RNIh+PjYH+GDcE0t9H2qRlGHvBTl6j4l9SZ1a1aarSCZbq5QMN4JWBsfhP0cfDjNw9MYD1VH0j7pf3tJ10g6EZ8D7wv8svNfwNsRjEMdrQCMl9QDeACvQ96BNBFDWXO2FEUJeSlLTdVOw0eYaonf/Z0IXFigo0d+T7zdgSfN7OHUlfos4FwzG1rKc1gS6UepCX4zMnfD8htgKD6c5pPAEGAbM7smt09UVYSsTHv1tfHJfScC3YB2eFXXI3jro6rrGFWbCMhLQV5g3R3/Be+VetO1w2cCeTw7SE7upl0ao6IDMBDvSn0Q/uv/KDDbfNCdqiGphZlNTcvnAYcCm5nZVEnbAScD15jZK2mbqqsPD6UhqRPeeWgE8Ck+t+IUvFrvwdQUdJkSAXkJZX7JGwNNzYfGXB+4AnjGzK6sZd9WwAvAs8Au+PCZs/Fuofel9rlVQ1ITYB/gB/xH5r94x5atgBPNbEAq8Qy3Cpl2PVSeTGeq04EWZnZ2qqrYGW+D3Kya2xrXJgLyUiLpHrwH3Xi8fvRbvKT8hJldV8M+pwAT8Prmz/BxHY4G3kqdJ6qOpDXx2RnWMrMOKe0AvPrlL2b235QW1RRhPgWafm4J/BS/8TtFPvb3f83swbJlssgiIC8F8kGwGwN3A7fiA6s/kdoczzfQfN5+K+KTd96NDza0N7AHsF+1NgNL3aL/jN+E+cbMLk7pWwKNzOyNcuYvVD5Jh+Jt99/Eq/vG4Z0+ugH7WoExX5YVEZAXUzbQStoQ+BJvqvYe3nplE3wOr8lpm1bASmb2paTDgA2AG4GRePfPD/GJTY8yH3KyqoJxXj16Y3wAmFOBjnj1xWm19UoMAUA+qPzpeLXXG8Dj+GeoGTDI0pCby6po9raIcmMymE+3tH1qrtbMfIohAdOBXYHb8n7JfwHsn379DwPG4u1wNwXeBlYEzrZ54/9WRTBObYsh81kysx9THfHl+Di1r0QwDjXJfackbYQXZM4xs4OB5nhrpe/M7KllPRhDlJAXmaRfAwfgv+L/wJtxrY1PP/QV3hX4NTO7OW+/3IerIz69/WXySRh74014njCzH6upZJxXKn4EuNrMXqxl+2hREeaTuYG3Gt5rcwowCLjOzIZLOgH4wsyeKWtGSyTGslhEZnZzqn7oC9xuZldJWg84BljHzH6Z3V4+swd4W9w5eMeI9STtYmbPS/oen8Tzx3T8qgjGMN8YFX8CPjOfVHJPYDjejE+WGVw+gnHIl4JxQ+BE4On0fToF7xT1gpldW+YsllRUWdSRpFUk/TQ9vB+4EDhFPmDQp3g98JACu87G61I/AqaZ2S3AS8BPJR2B14s9XPQTKK7pQEdJl+FjPp8GrGIx00eom054x4/VUwHmWvxm9/plzVUZRAm57qYBO0u6EXjJzI6T9A1wvnyW6MuAO/N3MrPBku7CZ8VoLGk5M/uPfLSzVtXW8QPma3u9D7CpmV0oHx50OF5t8z98TryqnQU7lI752N598cHmewDvmdm1yoyhXV/UuxNeXGY2Hh9z9TtgtqQVzccrPhqvgig4FmuqI3sJ2IvU2kA+r15XfMD5qhv/NwXjNfGS/63ygZS+Mp8X7yHgLjP7oKyZDFUlDQ/QD2/itlXq8Vnvqrjipt4ikI/W1hzvjbYjfkNvOeBf2bbGtd2Ykw829BP8Jl5VDayj+YcA3QY4G/9R2QUvEV8DDDCzb9M2VXODMpRG5upqM3zihiZm9n5mfUt89vUxNR5kGRYBuRaqYcbj1M52D3xchr6WZrpI63IfuM3x1het8YA9Pbt/FbaoyAbjDfAhQhsAW5rZ7ZIuxkezOz9tUzXnFkor3QS/D68r3hF4yMweKW+uKkNUWdQgM/hPe0nna/454X40s8eBntlgnNbNkQ8odD3eLXp34Ld521Rji4pcML4bOAK4BdguBeOeeNvrSzLbV825hdJJLSpOAo7Fq/86AftKOqusGasQEZBrkCkZX4ePNNVY0grpZlyuxDh3vIm8GxDb4D2MPserNO6QtI2k5UuT++KQtA4w2cxyM19PlI9W9wmwVyr1N6z1IKFeS9+rC/HPzG/wqcnewcdxqfciIOfJ3mCTtDo+M8EneIn3QnxM3/na1KbL81wJ8hC8G3RnfKr7U4CdgF+ZWbXPqjwZ6CCpH/6D8yT+usw2szHpdYimbqGg3I91uscgvOXSZfj342/ly1nliICcJ9PZoTfevfkKvIXERcDrwGHKm8Ujs8/B+DQzw/DWBi/il/dHA2embaqmRYWkIzIdWzCzEXhvqvXxGzJ3Av3M7Ku0PqopQo3yOgmNA84AxgC/jx9yFzf1CpB0ID6z7cG5m3HycXwfBo5Jzd3y91kFuACfzeBSvAtoC3zaou/NbERNNwkrUeqN2AtvEfK4mb2VWbc6sB0wpr50aQ2LLtMtel+8UPMmMMTS5ARhQRGQ80hqCtyFVzeMAfbFpx+6CVjPMlONZ1pUtMEv5zfBJ/EcCLxpVTT1UiEpKO8KbIlPs/6flH4E8JGlGRuiRUWoiaR18VENT8FLxC8BN9bHNsZ1EVUWzKvbks94MRM4F59ivCvwO7yJW5u8YKwUjFfHP3AP4AMHvQ6sCxwsH++4apnZJHzWjxeALSXtK6kPcIBlps+JYByyCvSwewxYHR8P+3q82m/l0ues8kUJOUl1u/cBXwMrmNkxKf1yPBj/pob97sMD1ovAL/HBgx4HtjCzp4uf8+JLX7DNgKPwTiA/NbNRitHbQp689uo74d+nE4HueOFmO2BzM+tdrjxWsgjISWoHOQWvrvgM2Bz4EdgnDQiU226+y3P59PZvmdnbqSff3cBxZvZdoe2rVfrB6owXiL+upvrwUHqpvfoXZnZe6tX5M3yy0q3N7PDy5q5y1fvBhTIBczT+evwdH+94JXxKpYuy22daVGwIDMNnCDlb0j+ANfCu1ePzt6926TyGZB5HMA4FpXFOGpjZeQBm9oakEeaDCDUrc/YqWr2sQ5bUInVtzgbMJ/FL8tXwjiDXAI/U0HX6l/jg9E/gU82cEMAy5gAACPtJREFUC2wBdAH+aGbTC9SjhbDMymvO+QPQST4aYO7G3pGSmlnMNl6relllIZ+p4+f4AD9PZtJXwCfo/B741Mz6ZdblWlSsjgfua/AS8UXA9Wb2WP62pTmbEMor07xtE/xG+Jf4qG2X4FdVOwLXmtm/y5jNqlBfA3JTYHt8ivHBwN25krCk7YEPUwuD3Pa5YLwm3ta4PfA3M3smtU++Ay8Zv1niUwmhrDJjvqwA3A68jAfg14Gr8O/YTDN7uYzZrBr1MiDD3JYDm+NN2iaYD4h9JTDWzC4osH1jfFaQN4BJeAn7/hSUG1sVDjQfwtKSbuJ9Y2Znpu/KjUBjfBb1uN9QR/X2pl4q8b6LTx2zq/T/7Z1/rNV1GcdfbzGRH8oyQzMqGqTmFiHyTy0aYLM2NKRAM7JIWoOtlSbYSsubf5Rttpq2IslgOKZEG/0AnQiNrLx5Me71XpjRMMCxRWqlcflxA3z643m+9eXs3HPvhQvnwHle22fn8/1+fnw/n3PPee5zns/n8zxqxT9Qt/XS5MO4ZnzYzB6T9Cpwh6TXzWwDnDk7KpKkL2LnxDigB+jA11QekrTNzFYC8yV9PoXxwGhaDblMHHv+GO7buLuirLCPnQXcgPtx+K15gNK34SHKUztOmgZJV+G/Fh/DbcVzgW8BXbivky4z+3r9Rnj6kgI5KGu3cWLvAjPbKw8lc6BUbwYwDXjaIjhpasZJsxC7Kb4DbCj8mEh6O+6mdgluQ34IuN3M9tRtoKcpTWuyqKRCoF4MfFbu+3gksLCwE5vZujBX7O2lbZKcscSvxR58i+f6UEZejINVM81sHXBjfUd5+pJ7ZasQToHacUdBB+PeYUlnR/4PZvZCHYeYJPVkBTBS0hW4X2Nw08VlkkbnHvzjJ9+4KoTgHQEsBvZJujuKrqzfqJKkYdiJn0adDnxCHnV8Eb4o/lLuwT9+UiAHFSeNrsYjhbyKu908JGkzfiAkSZoa8wjrS/FIOuPwvfmHzCzj4p0guahXgaQxuH14Bv5h2457cpsAbI5N8LmIlySBpHPM7D/1HseZQC7qlYgz918AfhVpBr5AMdzMVkedPBadJCVSGA8eKZCDcJ25Fw/TNAl4Ix4Ndzi+wAccG9w0SZJkMGlqG7Kkm2NVeByuEX8Oj4DbBozHo4CsNrMd9RtlkiTNQtPakCNe3M3AGOA9xAkjYA5wxMy+L+lyM/tzHYeZJEkT0bQacnhzexh4FneCMszMdgOP4j6RwRf0KndgJEmSnBSa2oZsZvsk/RoP3TQtfFocAC6Nciu/JkmSnEya1mRRJk4WvRf4Cq4tLzCzl3NHRZIkp5IUyCXC2Xx3OBVKYZwkySklBXKSJEmD0LSLekmSJI1GCuQkSZIGIQVykiRJg5ACOUmSpEFIgZzURNJRSR2lNLaOY3lEUqek3gLRnmj/8yRdUqP8HkkfivwuSRcOoO+xkj5Zup4s6f4TG/H/+vracbQZJWmFpBcirZA0KsrOknS/pK2SuiRtlvTOKLsl7nVG+czBmEPi5C6LpCaSus1sZI3ys8M/7skex8XAM2b2jpP4jE3AIjN7tkrZkHIEZUm7gMlm9ko/+54afV87OKM9pu+af6Ne2vwc2GpmLXH9TeAKM5sj6Sbg48ANEZ19DH54agQeM2+Smb0WIc7ebGY7B3M+zUxqyMmACU1ydZxyLAJdLg5NqjO+3EXdOyVtl7QhNNxFffR9rqRloYW1S5oWReuB0aGlT6loc5GkNZKei/T+uP/l0OK2Sro17o2V9LykpZK2SVovaZik2cBkYGU8Y1howd+Q9HtgjqTlUa9gsaS2SOOj/2PqSCqimN8LTIm+b5M0VdLaqHOBpF/Ee/dHSRPifoukn0raJOmvkr5Y5f26FxgW/a7sbd4VbcYDV+GO5QvuASbLHW29BfhbsQ/fzPaY2b+A0cA+oDvud6cwHmTMLFOmXhNwFOiItCbuzQP24JG5Aa7BI6sI/ye/Fvgg/qXvwl2Yng/swLXEWs+7HVgW+cuBF4FzgbG4RletzSrg1sgPAUaVnj0CD1S7DQ/BNRY4AkyM+j8DPhX5TbjWW/S7C7ijdL0cmF0quzPynwbWVtaJ6+54nVrUqbwGHgDujvx0oCPyLcDTwFDgQuAfwBuqzL+7lK8674r6Hy3+lhX310TZmJhfB/Ddon28t0/E32QZcF29P59nWkoNOemLg2Y2MdKs0v0nzeyfkb8mUjuwBRek7wKm4F/8A2b2b9zFaV98AHf6hLmnvd2Eb5EaTAd+FG2Omtlr0c8aM9tvZt24n+tCs95pZh2R/xMupHtjVY2yR0qv7+tjjLUoz/k3wJsKey6wzsx6zE0jLwEX9aOv3uZdIKCarVI+BNsDXAZ8FXgd2CjpanOTzUeA2cBfgO9JahnYVJNaNLVzoeSE2F/KC/i2mf24XCF+Lg90kWKwPOvV6qenlD8KDKtRd3+NMquSP0KYAiUJOKdG+4JqYy36qxxrX9/Z/rx/24Ary+4B9H9/Ls8DmFkP8DjwuKS/A9cDG81V5TagTdKTuKbc0o9nJv0gNeRkMHgCuCUWeZD0Vnkk4qeAWWGPPQ+4rh99PQXMjX4uxV2hbu+jzUZgYbQZIun86Od6ScMljQBmAb/ro599wHn9GGPBjaXX1sjvws0GADNxZ1V99V2e81TglfhF0V8OSyqe0+e8zQMutAN3lW7fBWwxsx2SJil2m4SgngDslnSJpEmlNhPxXzDJIJEacnLCmNl6Se8GWl0ppBu3y26RtAq3Re6mJBgkLYi2Syq6+yGwRFIXrm3OM7Me1XZJ/SXgQUnzcS1yoZm1SlqOa3MAPzGzdtXetrc8nn2Q/pkghkp6Bldsbop7S4FfSmrD/1EUGnYncETSc/Gc9lI/LcAySZ24+9fP9OPZZR4EOiVtMbO51eZdpc184AFJO3CtujXugS/eLZU0NK7bgB/g5pL7QlgfAl4GFgxwrEkNcttbcsoIe2O3md1X77EkSSOSJoskSZIGITXkJEmSBiE15CRJkgYhBXKSJEmDkAI5SZKkQUiBnCRJ0iCkQE6SJGkQUiAnSZI0CP8FIDDFE3obbbIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 4))\n",
    "splot = sns.barplot(x=\"OpenSourcer\", y=\"CompTotal\",\n",
    "                      data=salary_data,  palette=\"Blues_d\", capsize=0.1)\n",
    "# utils.plot_grouped_bars(salary_data, \"OpenSourcer\", \"Hobbyist\", \"CompTotal\",\n",
    "#                   title=\"What is the average salary of Open Sourcers?\",\n",
    "#                         xlabel=\"\", ylabel=\"# Respondents\")\n",
    "# splot.legend(loc='center left', bbox_to_anchor=(1.00, 0.5), ncol=1);\n",
    "plt.ylabel(\"Mean Salary\", size=8, fontweight='light')\n",
    "plt.xlabel(\"Freq. of contribution to OSS\")\n",
    "plt.xticks(rotation=50, size=8)\n",
    "# plt.tight_layout()\n",
    "plt.title(\"What is the average salary of Open Sourcers?\",\n",
    "          size=11, fontweight='light')\n",
    "\n",
    "for p in splot.patches:\n",
    "    splot.annotate(format(round(p.get_height() / 1000), '.0f') + \"K\", (p.get_x()+0.3, p.get_height()),\n",
    "        ha='center', va='center', \n",
    "        size=10, xytext=(0, -12),\n",
    "        textcoords='offset points', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}