data_science_blog_post/Data Science Blog.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gather Data - Libraries and input files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Professional</th>\n",
       "      <th>ProgramHobby</th>\n",
       "      <th>Country</th>\n",
       "      <th>University</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>FormalEducation</th>\n",
       "      <th>MajorUndergrad</th>\n",
       "      <th>HomeRemote</th>\n",
       "      <th>CompanySize</th>\n",
       "      <th>CompanyType</th>\n",
       "      <th>...</th>\n",
       "      <th>StackOverflowMakeMoney</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HighestEducationParents</th>\n",
       "      <th>Race</th>\n",
       "      <th>SurveyLong</th>\n",
       "      <th>QuestionsInteresting</th>\n",
       "      <th>QuestionsConfusing</th>\n",
       "      <th>InterestedAnswers</th>\n",
       "      <th>Salary</th>\n",
       "      <th>ExpectedSalary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Respondent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Student</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Not employed, and not looking for work</td>\n",
       "      <td>Secondary school</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>High school</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Student</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Employed part-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>More than half, but not all, the time</td>\n",
       "      <td>20 to 99 employees</td>\n",
       "      <td>Privately-held limited company, not in startup...</td>\n",
       "      <td>...</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A master's degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Professional developer</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor's degree</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>Less than half the time, but at least one day ...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>Publicly-traded corporation</td>\n",
       "      <td>...</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A professional degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>113750.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Professional non-developer who sometimes write...</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Doctoral degree</td>\n",
       "      <td>A non-computer-focused engineering discipline</td>\n",
       "      <td>Less than half the time, but at least one day ...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>Non-profit/non-governmental organization or pr...</td>\n",
       "      <td>...</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A doctoral degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Professional developer</td>\n",
       "      <td>Yes, I program as a hobby</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Master's degree</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>Never</td>\n",
       "      <td>10 to 19 employees</td>\n",
       "      <td>Privately-held limited company, not in startup...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 153 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Professional  \\\n",
       "Respondent                                                      \n",
       "1                                                     Student   \n",
       "2                                                     Student   \n",
       "3                                      Professional developer   \n",
       "4           Professional non-developer who sometimes write...   \n",
       "5                                      Professional developer   \n",
       "\n",
       "                         ProgramHobby         Country      University  \\\n",
       "Respondent                                                              \n",
       "1                           Yes, both   United States              No   \n",
       "2                           Yes, both  United Kingdom  Yes, full-time   \n",
       "3                           Yes, both  United Kingdom              No   \n",
       "4                           Yes, both   United States              No   \n",
       "5           Yes, I program as a hobby     Switzerland              No   \n",
       "\n",
       "                                  EmploymentStatus  \\\n",
       "Respondent                                           \n",
       "1           Not employed, and not looking for work   \n",
       "2                               Employed part-time   \n",
       "3                               Employed full-time   \n",
       "4                               Employed full-time   \n",
       "5                               Employed full-time   \n",
       "\n",
       "                                              FormalEducation  \\\n",
       "Respondent                                                      \n",
       "1                                            Secondary school   \n",
       "2           Some college/university study without earning ...   \n",
       "3                                           Bachelor's degree   \n",
       "4                                             Doctoral degree   \n",
       "5                                             Master's degree   \n",
       "\n",
       "                                           MajorUndergrad  \\\n",
       "Respondent                                                  \n",
       "1                                                     NaN   \n",
       "2                Computer science or software engineering   \n",
       "3                Computer science or software engineering   \n",
       "4           A non-computer-focused engineering discipline   \n",
       "5                Computer science or software engineering   \n",
       "\n",
       "                                                   HomeRemote  \\\n",
       "Respondent                                                      \n",
       "1                                                         NaN   \n",
       "2                       More than half, but not all, the time   \n",
       "3           Less than half the time, but at least one day ...   \n",
       "4           Less than half the time, but at least one day ...   \n",
       "5                                                       Never   \n",
       "\n",
       "                         CompanySize  \\\n",
       "Respondent                             \n",
       "1                                NaN   \n",
       "2                 20 to 99 employees   \n",
       "3           10,000 or more employees   \n",
       "4           10,000 or more employees   \n",
       "5                 10 to 19 employees   \n",
       "\n",
       "                                                  CompanyType  ...  \\\n",
       "Respondent                                                     ...   \n",
       "1                                                         NaN  ...   \n",
       "2           Privately-held limited company, not in startup...  ...   \n",
       "3                                 Publicly-traded corporation  ...   \n",
       "4           Non-profit/non-governmental organization or pr...  ...   \n",
       "5           Privately-held limited company, not in startup...  ...   \n",
       "\n",
       "           StackOverflowMakeMoney Gender HighestEducationParents  \\\n",
       "Respondent                                                         \n",
       "1               Strongly disagree   Male             High school   \n",
       "2               Strongly disagree   Male       A master's degree   \n",
       "3                        Disagree   Male   A professional degree   \n",
       "4                        Disagree   Male       A doctoral degree   \n",
       "5                             NaN    NaN                     NaN   \n",
       "\n",
       "                                    Race         SurveyLong  \\\n",
       "Respondent                                                    \n",
       "1           White or of European descent  Strongly disagree   \n",
       "2           White or of European descent     Somewhat agree   \n",
       "3           White or of European descent     Somewhat agree   \n",
       "4           White or of European descent              Agree   \n",
       "5                                    NaN                NaN   \n",
       "\n",
       "           QuestionsInteresting QuestionsConfusing  InterestedAnswers  \\\n",
       "Respondent                                                              \n",
       "1                Strongly agree           Disagree     Strongly agree   \n",
       "2                Somewhat agree           Disagree     Strongly agree   \n",
       "3                         Agree           Disagree              Agree   \n",
       "4                         Agree     Somewhat agree     Strongly agree   \n",
       "5                           NaN                NaN                NaN   \n",
       "\n",
       "              Salary ExpectedSalary  \n",
       "Respondent                           \n",
       "1                NaN            NaN  \n",
       "2                NaN        37500.0  \n",
       "3           113750.0            NaN  \n",
       "4                NaN            NaN  \n",
       "5                NaN            NaN  \n",
       "\n",
       "[5 rows x 153 columns]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read File containing survey data\n",
    "df = pd.read_csv('./survey_results_public.csv', index_col='Respondent')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assess - Let's Get A Sense of What the Data Looks Like\n",
    "\n",
    "##### From this section, we notice that there are 51,392 respondents to the 153 questions asked. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51392, 153) Index(['Professional', 'ProgramHobby', 'Country', 'University',\n",
      "       'EmploymentStatus', 'FormalEducation', 'MajorUndergrad', 'HomeRemote',\n",
      "       'CompanySize', 'CompanyType',\n",
      "       ...\n",
      "       'StackOverflowMakeMoney', 'Gender', 'HighestEducationParents', 'Race',\n",
      "       'SurveyLong', 'QuestionsInteresting', 'QuestionsConfusing',\n",
      "       'InterestedAnswers', 'Salary', 'ExpectedSalary'],\n",
      "      dtype='object', length=153)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape, df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Country',\n",
       " 'EmploymentStatus',\n",
       " 'FormalEducation',\n",
       " 'Professional',\n",
       " 'ProgramHobby',\n",
       " 'University'}"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Columns with no null values\n",
    "no_nulls = set(df.columns[df.isnull().mean()==0])\n",
    "\n",
    "no_nulls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Only 6 out of 153 columns columns have no null values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Over 50% null values\n",
    "more_than_half_missing_cols = len(set(df.columns[df.isnull().mean() > 0.50]))\n",
    "more_than_half_missing_cols\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see that 53 questions have missing values in more than half of their rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ExCoder10Years',\n",
       " 'ExCoderActive',\n",
       " 'ExCoderBalance',\n",
       " 'ExCoderBelonged',\n",
       " 'ExCoderNotForMe',\n",
       " 'ExCoderReturn',\n",
       " 'ExCoderSkills',\n",
       " 'ExCoderWillNotCode',\n",
       " 'ExpectedSalary',\n",
       " 'MobileDeveloperType',\n",
       " 'NonDeveloperType',\n",
       " 'TimeAfterBootcamp',\n",
       " 'YearsCodedJobPast'}"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#More than 80% null values\n",
    "most_missing_cols = set(df.columns[df.isnull().mean() > 0.80])\n",
    "most_missing_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see that 13 questions have missing values in more than 80% of their rows. They are good candidates for dropping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CareerSatisfaction',\n",
       " 'ClickyKeys',\n",
       " 'Country',\n",
       " 'EmploymentStatus',\n",
       " 'FormalEducation',\n",
       " 'HomeRemote',\n",
       " 'MajorUndergrad',\n",
       " 'Professional',\n",
       " 'ProgramHobby',\n",
       " 'PronounceGIF',\n",
       " 'University',\n",
       " 'YearsProgram'}"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "least_missing_cols = set(df.columns[df.isnull().mean() < 0.20])\n",
    "least_missing_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see that 12 questions have missing values in less than 20% of their rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean, Analyze and Visualize (We'll be using bar charts to visualize data)\n",
    "### Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. What are the 5 most represented countries and Majors? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "201"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = len(set(df.Country))\n",
    "\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are 201 countries represented in the dataset. Every respondent answered this question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States     11455\n",
       "India              5197\n",
       "United Kingdom     4395\n",
       "Germany            4143\n",
       "Canada             2233\n",
       "                  ...  \n",
       "Macau                 1\n",
       "Gibraltar             1\n",
       "Seychelles            1\n",
       "Turkmenistan          1\n",
       "Montserrat            1\n",
       "Length: 201, dtype: int64"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_count = pd.value_counts(df['Country'].values, sort=True)\n",
    "\n",
    "country_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see the top countries are United States, India, United Kingdom, Germany and Canada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Computer science or software engineering                        21409\n",
       "Computer engineering or electrical/electronics engineering       4364\n",
       "Computer programming or Web development                          3883\n",
       "Information technology, networking, or system administration     2140\n",
       "A natural science                                                1865\n",
       "A non-computer-focused engineering discipline                    1789\n",
       "Mathematics or statistics                                        1648\n",
       "Something else                                                   1060\n",
       "A humanities discipline                                           901\n",
       "A business discipline                                             900\n",
       "Management information systems                                    654\n",
       "Fine arts or performing arts                                      646\n",
       "A social science                                                  633\n",
       "I never declared a major                                          592\n",
       "Psychology                                                        215\n",
       "A health science                                                  142\n",
       "dtype: int64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "major_undergrad_count = pd.value_counts(df['MajorUndergrad'].values, sort=True)\n",
    "major_undergrad_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see the top majors are Computer science or software engineering and Computer engineering or electrical/electronics engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Select top 5 countries for further questions\n",
    "selected_countries = ['United States', 'India', 'United Kingdom', 'Germany', 'Canada']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_countries_df = df[df['Country'].map(lambda x: x in selected_countries)].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "      <th>Professional</th>\n",
       "      <th>ProgramHobby</th>\n",
       "      <th>Country</th>\n",
       "      <th>University</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>FormalEducation</th>\n",
       "      <th>MajorUndergrad</th>\n",
       "      <th>HomeRemote</th>\n",
       "      <th>CompanySize</th>\n",
       "      <th>...</th>\n",
       "      <th>StackOverflowMakeMoney</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HighestEducationParents</th>\n",
       "      <th>Race</th>\n",
       "      <th>SurveyLong</th>\n",
       "      <th>QuestionsInteresting</th>\n",
       "      <th>QuestionsConfusing</th>\n",
       "      <th>InterestedAnswers</th>\n",
       "      <th>Salary</th>\n",
       "      <th>ExpectedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Student</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Not employed, and not looking for work</td>\n",
       "      <td>Secondary school</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>High school</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Student</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Employed part-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>More than half, but not all, the time</td>\n",
       "      <td>20 to 99 employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Strongly disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A master's degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Professional developer</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor's degree</td>\n",
       "      <td>Computer science or software engineering</td>\n",
       "      <td>Less than half the time, but at least one day ...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A professional degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>113750.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Professional non-developer who sometimes write...</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Doctoral degree</td>\n",
       "      <td>A non-computer-focused engineering discipline</td>\n",
       "      <td>Less than half the time, but at least one day ...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A doctoral degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Somewhat agree</td>\n",
       "      <td>Strongly agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Professional non-developer who sometimes write...</td>\n",
       "      <td>Yes, both</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Master's degree</td>\n",
       "      <td>A non-computer-focused engineering discipline</td>\n",
       "      <td>Less than half the time, but at least one day ...</td>\n",
       "      <td>20 to 99 employees</td>\n",
       "      <td>...</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Male</td>\n",
       "      <td>A doctoral degree</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 154 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent                                       Professional ProgramHobby  \\\n",
       "0           1                                            Student    Yes, both   \n",
       "1           2                                            Student    Yes, both   \n",
       "2           3                             Professional developer    Yes, both   \n",
       "3           4  Professional non-developer who sometimes write...    Yes, both   \n",
       "4           7  Professional non-developer who sometimes write...    Yes, both   \n",
       "\n",
       "          Country      University                        EmploymentStatus  \\\n",
       "0   United States              No  Not employed, and not looking for work   \n",
       "1  United Kingdom  Yes, full-time                      Employed part-time   \n",
       "2  United Kingdom              No                      Employed full-time   \n",
       "3   United States              No                      Employed full-time   \n",
       "4   United States              No                      Employed full-time   \n",
       "\n",
       "                                     FormalEducation  \\\n",
       "0                                   Secondary school   \n",
       "1  Some college/university study without earning ...   \n",
       "2                                  Bachelor's degree   \n",
       "3                                    Doctoral degree   \n",
       "4                                    Master's degree   \n",
       "\n",
       "                                  MajorUndergrad  \\\n",
       "0                                            NaN   \n",
       "1       Computer science or software engineering   \n",
       "2       Computer science or software engineering   \n",
       "3  A non-computer-focused engineering discipline   \n",
       "4  A non-computer-focused engineering discipline   \n",
       "\n",
       "                                          HomeRemote  \\\n",
       "0                                                NaN   \n",
       "1              More than half, but not all, the time   \n",
       "2  Less than half the time, but at least one day ...   \n",
       "3  Less than half the time, but at least one day ...   \n",
       "4  Less than half the time, but at least one day ...   \n",
       "\n",
       "                CompanySize  ... StackOverflowMakeMoney Gender  \\\n",
       "0                       NaN  ...      Strongly disagree   Male   \n",
       "1        20 to 99 employees  ...      Strongly disagree   Male   \n",
       "2  10,000 or more employees  ...               Disagree   Male   \n",
       "3  10,000 or more employees  ...               Disagree   Male   \n",
       "4        20 to 99 employees  ...               Disagree   Male   \n",
       "\n",
       "  HighestEducationParents                          Race         SurveyLong  \\\n",
       "0             High school  White or of European descent  Strongly disagree   \n",
       "1       A master's degree  White or of European descent     Somewhat agree   \n",
       "2   A professional degree  White or of European descent     Somewhat agree   \n",
       "3       A doctoral degree  White or of European descent              Agree   \n",
       "4       A doctoral degree  White or of European descent           Disagree   \n",
       "\n",
       "  QuestionsInteresting QuestionsConfusing InterestedAnswers    Salary  \\\n",
       "0       Strongly agree           Disagree    Strongly agree       NaN   \n",
       "1       Somewhat agree           Disagree    Strongly agree       NaN   \n",
       "2                Agree           Disagree             Agree  113750.0   \n",
       "3                Agree     Somewhat agree    Strongly agree       NaN   \n",
       "4                Agree           Disagree             Agree       NaN   \n",
       "\n",
       "   ExpectedSalary  \n",
       "0             NaN  \n",
       "1         37500.0  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "\n",
       "[5 rows x 154 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_countries_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We confirm that we have only 5 countries in our dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5336044520547946"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(selected_countries_df)/len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Notice that the top 5 countries account for 53% of the responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'United States': 11455,\n",
       " 'India': 5197,\n",
       " 'United Kingdom': 4395,\n",
       " 'Germany': 4143,\n",
       " 'Canada': 2233}"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## create a function to tabulate results by countries for analysis and plotting\n",
    "\n",
    "\"\"\" Object to return value counts of top 5 countries.\"\"\"\n",
    "\n",
    "sumByCountry = {'United States': selected_countries_df.Country.value_counts()['United States'],\n",
    "              'India': selected_countries_df.Country.value_counts()['India'],\n",
    "              'United Kingdom': selected_countries_df.Country.value_counts()['United Kingdom'],\n",
    "              'Germany': selected_countries_df.Country.value_counts()['Germany'],\n",
    "              'Canada': selected_countries_df.Country.value_counts()['Canada'],\n",
    "             } \n",
    "def groupByCountry(column, selected_df=selected_countries_df):\n",
    "    \"\"\"\n",
    "    Function to group data by selected countries\n",
    "    \n",
    "    Parameters:\n",
    "    \n",
    "    column - Column to group by country\n",
    "    selected_df - A dataframe containing column\n",
    "    \n",
    "    Returns:\n",
    "    Dataframe containing the grouped data\n",
    "    \"\"\"\n",
    "    df = selected_df[['Country', column]].dropna(axis=0, how='any')\n",
    "    expanded = df[column].str.get_dummies(';')\n",
    "    fulldf = pd.concat([df['Country'], expanded], axis=1).groupby('Country').sum().T.rename_axis(column)\n",
    "    for column in list(fulldf.columns):\n",
    "        fulldf[column] =fulldf[column].apply(lambda x: round((x/sumByCountry[column]), 3))\n",
    "    return fulldf\n",
    "\n",
    "sumByCountry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_proportion = pd.value_counts(selected_countries_df['Country'].values, sort=True)/len(selected_countries_df)\n",
    "\n",
    "plt.figure(figsize = (20, 7))\n",
    "country_proportion.plot(kind='bar', title='Distribution of Developers by Country')\n",
    "plt.ylabel('Number')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. What's the remote work culture like in the selected countries? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "remote_culture_proportion = pd.value_counts(selected_countries_df['HomeRemote'].values, sort=True)/len(selected_countries_df)\n",
    "\n",
    "plt.figure(figsize = (20, 7))\n",
    "remote_culture_proportion.plot(kind='bar', title='How often do developers work from home?')\n",
    "plt.ylabel('Number')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Germany</th>\n",
       "      <th>India</th>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>United States</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HomeRemote</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A few days each month</th>\n",
       "      <td>0.344</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.316</td>\n",
       "      <td>0.342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>About half the time</th>\n",
       "      <td>0.027</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All or almost all the time (I'm full-time remote)</th>\n",
       "      <td>0.088</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>It's complicated</th>\n",
       "      <td>0.045</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Less than half the time, but at least one day each week</th>\n",
       "      <td>0.067</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>More than half, but not all, the time</th>\n",
       "      <td>0.030</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Never</th>\n",
       "      <td>0.258</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country                                             Canada  Germany  India  \\\n",
       "HomeRemote                                                                   \n",
       "A few days each month                                0.344    0.312  0.271   \n",
       "About half the time                                  0.027    0.030  0.038   \n",
       "All or almost all the time (I'm full-time remote)    0.088    0.054  0.079   \n",
       "It's complicated                                     0.045    0.033  0.019   \n",
       "Less than half the time, but at least one day e...   0.067    0.077  0.074   \n",
       "More than half, but not all, the time                0.030    0.029  0.055   \n",
       "Never                                                0.258    0.347  0.272   \n",
       "\n",
       "Country                                             United Kingdom  \\\n",
       "HomeRemote                                                           \n",
       "A few days each month                                        0.316   \n",
       "About half the time                                          0.023   \n",
       "All or almost all the time (I'm full-time remote)            0.078   \n",
       "It's complicated                                             0.050   \n",
       "Less than half the time, but at least one day e...           0.075   \n",
       "More than half, but not all, the time                        0.024   \n",
       "Never                                                        0.317   \n",
       "\n",
       "Country                                             United States  \n",
       "HomeRemote                                                         \n",
       "A few days each month                                       0.342  \n",
       "About half the time                                         0.031  \n",
       "All or almost all the time (I'm full-time remote)           0.112  \n",
       "It's complicated                                            0.048  \n",
       "Less than half the time, but at least one day e...          0.096  \n",
       "More than half, but not all, the time                       0.030  \n",
       "Never                                                       0.215  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_remote_df = groupByCountry('HomeRemote')\n",
    "home_remote_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.figure(figsize = (20, 10))\n",
    "\n",
    "home_remote_df.plot(kind='bar')\n",
    "\n",
    "plt.title('Remote Culture by Country')\n",
    "plt.xlabel('How often do you work from home or remotely?')\n",
    "plt.ylabel('Proportion')\n",
    "plt.legend(title='Country')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Findings\n",
    "#### We see that most respondents rarely work at home. Remote work culture still has a lot of room to grow. \n",
    "#### More than half of the respondents never worked from home or only worked for a few days a month\n",
    "#### It was highest in Germany and lowest in India\n",
    "#### Over one-third of developers worked from home at least a few days a month and almost a third never worked from home"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. What percentage of developers program as a hobby by country? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "program_hobby = pd.value_counts(selected_countries_df['ProgramHobby'].values, sort=True)/len(selected_countries_df)\n",
    "\n",
    "plt.figure(figsize = (20, 10))\n",
    "\n",
    "program_hobby.plot(kind='bar', title='Program as a hobby')\n",
    "plt.ylabel('Number')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see that about 75% of developers code as a hobby and about 6% contribute to open source. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Germany</th>\n",
       "      <th>India</th>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>United States</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProgramHobby</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>0.203</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes, I contribute to open source projects</th>\n",
       "      <td>0.039</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes, I program as a hobby</th>\n",
       "      <td>0.525</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.401</td>\n",
       "      <td>0.542</td>\n",
       "      <td>0.489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes, both</th>\n",
       "      <td>0.233</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.323</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country                                    Canada  Germany  India  \\\n",
       "ProgramHobby                                                        \n",
       "No                                          0.203    0.168  0.191   \n",
       "Yes, I contribute to open source projects   0.039    0.061  0.085   \n",
       "Yes, I program as a hobby                   0.525    0.500  0.401   \n",
       "Yes, both                                   0.233    0.270  0.323   \n",
       "\n",
       "Country                                    United Kingdom  United States  \n",
       "ProgramHobby                                                              \n",
       "No                                                  0.195          0.185  \n",
       "Yes, I contribute to open source projects           0.042          0.044  \n",
       "Yes, I program as a hobby                           0.542          0.489  \n",
       "Yes, both                                           0.221          0.282  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "program_hobby_df = groupByCountry('ProgramHobby')\n",
    "program_hobby_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Notice that developers in India are twice as likely to contribute to open-source projects as developers in United Kingdom and Canada\n",
    "#### However, India has the lowest values for those who code as a hobby. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ffac070a350>"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.figure(figsize = (20, 10))\n",
    "\n",
    "program_hobby_df.plot(kind='bar')\n",
    "\n",
    "plt.title('Programming as a hobby by Country')\n",
    "plt.xlabel('Do you Program as a hobby?')\n",
    "plt.ylabel('Proportion')\n",
    "plt.legend(title='Country')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. What is the gender distribution by country? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender = len(set(selected_countries_df.Gender))\n",
    "gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Due to the large number of gender types, we will focus on just the top 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male                                                       17009\n",
       "Female                                                      1601\n",
       "Gender non-conforming                                        100\n",
       "Other                                                         95\n",
       "Male; Other                                                   80\n",
       "Female; Transgender                                           44\n",
       "Transgender                                                   35\n",
       "Male; Gender non-conforming                                   31\n",
       "Female; Gender non-conforming                                 23\n",
       "Male; Transgender                                              9\n",
       "Transgender; Gender non-conforming                             9\n",
       "Female; Transgender; Gender non-conforming                     8\n",
       "Male; Female                                                   6\n",
       "Male; Female; Transgender; Gender non-conforming; Other        4\n",
       "Gender non-conforming; Other                                   3\n",
       "Male; Female; Transgender; Gender non-conforming               2\n",
       "Male; Female; Transgender                                      2\n",
       "Male; Gender non-conforming; Other                             1\n",
       "Male; Transgender; Other                                       1\n",
       "Male; Transgender; Gender non-conforming                       1\n",
       "Male; Female; Transgender; Other                               1\n",
       "Male; Female; Gender non-conforming                            1\n",
       "Female; Transgender; Other                                     1\n",
       "Male; Female; Gender non-conforming; Other                     1\n",
       "Female; Other                                                  1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender = pd.value_counts(selected_countries_df['Gender'].values, sort=True)\n",
    "gender\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 4 genders are 'Male', 'Female', 'Other', 'Gender non-conforming'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male                     17009\n",
       "Female                    1601\n",
       "Gender non-conforming      100\n",
       "Other                       95\n",
       "dtype: int64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_gender_types_df = selected_countries_df[selected_countries_df['Gender'].map(\n",
    "        lambda x : x in ('Male','Female', 'Other', 'Gender non-conforming'))]\n",
    "top_gender_values = pd.value_counts(top_gender_types_df['Gender'].values, sort=True)\n",
    "top_gender_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_gender_values = pd.value_counts(top_gender_types_df['Gender'].values, sort=True)/len(selected_countries_df)*100\n",
    "\n",
    "plt.figure(figsize = (20, 7))\n",
    "top_gender_values.plot(kind='bar', title='Gender Distribution by Percentage')\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Percentage')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We notice that there are 4 times more Males than Females in the industry. \n",
    "#### It's quite clear that females are quite underrepresented. \n",
    "#### Let's see if the difference varies by country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Germany</th>\n",
       "      <th>India</th>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>United States</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>0.067</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender non-conforming</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.648</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>0.003</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country                Canada  Germany  India  United Kingdom  United States\n",
       "Gender                                                                      \n",
       "Female                  0.067    0.037  0.044           0.050          0.074\n",
       "Gender non-conforming   0.004    0.004  0.001           0.004          0.005\n",
       "Male                    0.648    0.661  0.466           0.672          0.650\n",
       "Other                   0.003    0.005  0.001           0.003          0.004"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_gender_values_by_country = groupByCountry('Gender', top_gender_types_df)\n",
    "top_gender_values_by_country\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ffac094fed0>"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.figure(figsize = (20, 10))\n",
    "\n",
    "top_gender_values_by_country.plot(kind='bar')\n",
    "\n",
    "plt.title('Gender Distribution by Country')\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Proportion')\n",
    "plt.legend(title='Country')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see that in 4 of the top 5 countries, about 65% of developers are Male. \n",
    "#### This percentage is quite lower in India at about 46%\n",
    "#### United states has twice more Female developers than Germany"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. What kind of formal education do developers tend to have? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bachelor's degree                                                    0.462531\n",
       "Master's degree                                                      0.195675\n",
       "Some college/university study without earning a bachelor's degree    0.160814\n",
       "Secondary school                                                     0.104657\n",
       "Doctoral degree                                                      0.026219\n",
       "I prefer not to answer                                               0.019072\n",
       "Primary/elementary school                                            0.018598\n",
       "Professional degree                                                  0.006564\n",
       "I never completed any formal education                               0.005871\n",
       "dtype: float64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education = pd.value_counts(selected_countries_df['FormalEducation'].values, sort=True)/len(selected_countries_df)\n",
    "education"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We notice that almost half of developer's earn a bachelor's degree and about 20% have earned a Master's degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 10))\n",
    "\n",
    "formal_education = pd.value_counts(selected_countries_df['FormalEducation'].values, sort=True)/len(selected_countries_df)*100\n",
    "\n",
    "formal_education.plot(kind='bar', title='Formal Education by Percentage')\n",
    "plt.xlabel('Formal Education')\n",
    "plt.ylabel('Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Germany</th>\n",
       "      <th>India</th>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>United States</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FormalEducation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bachelor's degree</th>\n",
       "      <td>0.452</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.613</td>\n",
       "      <td>0.425</td>\n",
       "      <td>0.495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doctoral degree</th>\n",
       "      <td>0.017</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I never completed any formal education</th>\n",
       "      <td>0.006</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I prefer not to answer</th>\n",
       "      <td>0.020</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master's degree</th>\n",
       "      <td>0.107</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primary/elementary school</th>\n",
       "      <td>0.025</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professional degree</th>\n",
       "      <td>0.008</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Secondary school</th>\n",
       "      <td>0.099</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Some college/university study without earning a bachelor's degree</th>\n",
       "      <td>0.266</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country                                             Canada  Germany  India  \\\n",
       "FormalEducation                                                              \n",
       "Bachelor's degree                                    0.452    0.230  0.613   \n",
       "Doctoral degree                                      0.017    0.037  0.003   \n",
       "I never completed any formal education               0.006    0.005  0.009   \n",
       "I prefer not to answer                               0.020    0.026  0.024   \n",
       "Master's degree                                      0.107    0.304  0.212   \n",
       "Primary/elementary school                            0.025    0.010  0.009   \n",
       "Professional degree                                  0.008    0.008  0.010   \n",
       "Secondary school                                     0.099    0.234  0.076   \n",
       "Some college/university study without earning a...   0.266    0.146  0.044   \n",
       "\n",
       "Country                                             United Kingdom  \\\n",
       "FormalEducation                                                      \n",
       "Bachelor's degree                                            0.425   \n",
       "Doctoral degree                                              0.041   \n",
       "I never completed any formal education                       0.007   \n",
       "I prefer not to answer                                       0.013   \n",
       "Master's degree                                              0.195   \n",
       "Primary/elementary school                                    0.016   \n",
       "Professional degree                                          0.006   \n",
       "Secondary school                                             0.118   \n",
       "Some college/university study without earning a...           0.178   \n",
       "\n",
       "Country                                             United States  \n",
       "FormalEducation                                                    \n",
       "Bachelor's degree                                           0.495  \n",
       "Doctoral degree                                             0.029  \n",
       "I never completed any formal education                      0.004  \n",
       "I prefer not to answer                                      0.016  \n",
       "Master's degree                                             0.167  \n",
       "Primary/elementary school                                   0.026  \n",
       "Professional degree                                         0.004  \n",
       "Secondary school                                            0.067  \n",
       "Some college/university study without earning a...          0.192  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formal_education_values_by_country = groupByCountry('FormalEducation')\n",
    "formal_education_values_by_country\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ffac07f5c10>"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "plt.figure(figsize = (20, 10))\n",
    "\n",
    "formal_education_values_by_country.plot(kind='bar')\n",
    "plt.title('Formal Education by Country')\n",
    "plt.xlabel('Formal Education')\n",
    "plt.ylabel('Proportion')\n",
    "plt.legend(title='Country')\n",
    "\n",
    "## We see "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Notice that developers tend to earn different degrees in different countries. In India, over 60% earn a Bachelor's degree while only 23% in Germany (less than half of India's percentage)\n",
    "#### Developers in India rarely earn Doctoral Degree (0.3%) compared to Germany and United States with over 10 times that amount\n",
    "#### 3 times more people earned Master's degree in Germany than Canada\n",
    "#### The top types of formal education in Canada was Bachelor's Degree, Some College/University and Master's degree\n",
    "#### The top types of formal education in Germany was Master's degree, Secondary School, and  Bachelor's Degree\n",
    "#### The top types of formal education in India was Bachelor's Degree, and Master's degree\n",
    "#### The top types of formal education in United Kingdom was Bachelor's Degree, Master's degree and Some College/University\n",
    "#### The top types of formal education in United States was Bachelor's Degree, Some College/University and Master's degree"
   ]
  },
  {
   "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
}