demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fresc\\AppData\\Local\\Programs\\Python\\Python37-32\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3173: DtypeWarning: Columns (8,12,13,14,15,16,50,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(53544, 11)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import chi2_contingency\n",
    "import numpy as np\n",
    "df = pd.read_csv('dataset/survey_results_public.csv', sep=',')\n",
    "js =  df['JobSatisfaction']\n",
    "# salary = df['Salary']\n",
    "# salarytype = df['SalaryType']\n",
    "df  = df.iloc[: , :10]\n",
    "df['JobSatisfaction']=js\n",
    "# df['Salary']=salary\n",
    "# df['SalaryType']=salarytype\n",
    "df = df.dropna()\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",
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       "    }\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>Hobby</th>\n",
       "      <th>OpenSource</th>\n",
       "      <th>Country</th>\n",
       "      <th>Student</th>\n",
       "      <th>Employment</th>\n",
       "      <th>FormalEducation</th>\n",
       "      <th>UndergradMajor</th>\n",
       "      <th>CompanySize</th>\n",
       "      <th>DevType</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed part-time</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Mathematics or statistics</td>\n",
       "      <td>20 to 99 employees</td>\n",
       "      <td>Full-stack developer</td>\n",
       "      <td>Extremely satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>A natural science (ex. biology, chemistry, phy...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>Database administrator;DevOps specialist;Full-...</td>\n",
       "      <td>Moderately dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Associate degree</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>20 to 99 employees</td>\n",
       "      <td>Engineering manager;Full-stack developer</td>\n",
       "      <td>Moderately satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>100 to 499 employees</td>\n",
       "      <td>Full-stack developer</td>\n",
       "      <td>Neither satisfied nor dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Yes, part-time</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>Data or business analyst;Desktop or enterprise...</td>\n",
       "      <td>Slightly satisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent Hobby OpenSource         Country         Student  \\\n",
       "0           1   Yes         No           Kenya              No   \n",
       "1           3   Yes        Yes  United Kingdom              No   \n",
       "2           4   Yes        Yes   United States              No   \n",
       "3           5    No         No   United States              No   \n",
       "4           7   Yes         No    South Africa  Yes, part-time   \n",
       "\n",
       "           Employment                                    FormalEducation  \\\n",
       "0  Employed part-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "1  Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "2  Employed full-time                                   Associate degree   \n",
       "3  Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "4  Employed full-time  Some college/university study without earning ...   \n",
       "\n",
       "                                      UndergradMajor  \\\n",
       "0                          Mathematics or statistics   \n",
       "1  A natural science (ex. biology, chemistry, phy...   \n",
       "2  Computer science, computer engineering, or sof...   \n",
       "3  Computer science, computer engineering, or sof...   \n",
       "4  Computer science, computer engineering, or sof...   \n",
       "\n",
       "                CompanySize  \\\n",
       "0        20 to 99 employees   \n",
       "1  10,000 or more employees   \n",
       "2        20 to 99 employees   \n",
       "3      100 to 499 employees   \n",
       "4  10,000 or more employees   \n",
       "\n",
       "                                             DevType  \\\n",
       "0                               Full-stack developer   \n",
       "1  Database administrator;DevOps specialist;Full-...   \n",
       "2           Engineering manager;Full-stack developer   \n",
       "3                               Full-stack developer   \n",
       "4  Data or business analyst;Desktop or enterprise...   \n",
       "\n",
       "                      JobSatisfaction  \n",
       "0                 Extremely satisfied  \n",
       "1             Moderately dissatisfied  \n",
       "2                Moderately satisfied  \n",
       "3  Neither satisfied nor dissatisfied  \n",
       "4                  Slightly satisfied  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Kenya', 'United Kingdom', 'United States', 'South Africa',\n",
       "       'Nigeria', 'Spain', 'India', 'Israel', 'Sweden', 'Australia',\n",
       "       'Greece', 'Poland', 'Argentina', 'Indonesia', 'Germany', 'Ireland',\n",
       "       'France', 'Ukraine', 'Denmark', 'China', 'Latvia', 'Algeria',\n",
       "       'Colombia', 'Japan', 'Russian Federation', 'Finland', 'Romania',\n",
       "       'Brazil', 'Bulgaria', 'Pakistan', 'Jordan', 'Portugal', 'Italy',\n",
       "       'New Zealand', 'Turkey', 'Iran, Islamic Republic of...', 'Austria',\n",
       "       'Egypt', 'Netherlands', 'Belgium', 'Canada', 'Slovakia', 'Croatia',\n",
       "       'Mexico', 'Norway', 'Czech Republic', 'Switzerland', 'Armenia',\n",
       "       'Bangladesh', 'United Arab Emirates', 'Singapore',\n",
       "       'Dominican Republic', 'Malaysia', 'Bahrain', 'Sri Lanka', 'Serbia',\n",
       "       'Botswana', 'Paraguay', 'Georgia', 'Uruguay', 'Belarus',\n",
       "       'Lithuania', 'Thailand', 'Estonia', 'Hungary', 'Malta', 'Myanmar',\n",
       "       'Costa Rica', 'Nepal', 'Hong Kong (S.A.R.)', 'Philippines',\n",
       "       'Bosnia and Herzegovina', 'Ghana', 'Taiwan', 'Lebanon', 'Peru',\n",
       "       'Saudi Arabia', 'Chile', 'Viet Nam', 'Slovenia', 'Uzbekistan',\n",
       "       'El Salvador', 'Cambodia', 'South Korea',\n",
       "       'Other Country (Not Listed Above)', 'Tunisia',\n",
       "       'The former Yugoslav Republic of Macedonia', 'Morocco', 'Qatar',\n",
       "       'Madagascar', 'Rwanda', 'Yemen', 'Uganda', 'Cuba', 'Ethiopia',\n",
       "       'Iceland', 'Oman', 'Ecuador', 'United Republic of Tanzania',\n",
       "       'Republic of Moldova', 'Honduras', 'Azerbaijan', 'Cyprus',\n",
       "       'Republic of Korea', 'Nicaragua', 'Kazakhstan', 'Kuwait',\n",
       "       'Cameroon', 'Venezuela, Bolivarian Republic of...', 'Albania',\n",
       "       'Afghanistan', 'Syrian Arab Republic', 'Jamaica', 'Guatemala',\n",
       "       'Zimbabwe', 'San Marino', 'Luxembourg', 'Fiji', 'Mozambique',\n",
       "       'Burundi', 'Bolivia', 'Liechtenstein', 'Montenegro', 'Sudan',\n",
       "       'Maldives', 'Turkmenistan', 'Panama', 'Mauritius', 'Barbados',\n",
       "       'Lesotho', 'Somalia', 'Mongolia', 'Gabon', 'Kyrgyzstan',\n",
       "       'Swaziland', 'Marshall Islands', 'Senegal', 'Iraq', 'Haiti',\n",
       "       'Malawi', 'Trinidad and Tobago', 'Libyan Arab Jamahiriya',\n",
       "       'Namibia', 'Tajikistan', 'Bhutan', 'Guinea', \"Côte d'Ivoire\",\n",
       "       'Bahamas', 'Andorra', 'Monaco', 'Gambia', 'Sierra Leone', 'Togo',\n",
       "       'Grenada', 'Benin', 'Burkina Faso',\n",
       "       'Democratic Republic of the Congo', 'Belize',\n",
       "       'Congo, Republic of the...', 'Antigua and Barbuda'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Country.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Hobby</th>\n",
       "      <th>OpenSource</th>\n",
       "      <th>Country</th>\n",
       "      <th>Student</th>\n",
       "      <th>Employment</th>\n",
       "      <th>FormalEducation</th>\n",
       "      <th>UndergradMajor</th>\n",
       "      <th>CompanySize</th>\n",
       "      <th>DevType</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Associate degree</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>20 to 99 employees</td>\n",
       "      <td>Engineering manager;Full-stack developer</td>\n",
       "      <td>Moderately satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>100 to 499 employees</td>\n",
       "      <td>Full-stack developer</td>\n",
       "      <td>Neither satisfied nor dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Yes, part-time</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>Data or business analyst;Desktop or enterprise...</td>\n",
       "      <td>Slightly satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>Back-end developer;Front-end developer;Full-st...</td>\n",
       "      <td>Slightly satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Fine arts or performing arts (ex. graphic desi...</td>\n",
       "      <td>100 to 499 employees</td>\n",
       "      <td>Back-end developer;C-suite executive (CEO, CTO...</td>\n",
       "      <td>Moderately satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71525</th>\n",
       "      <td>101585</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>India</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>1,000 to 4,999 employees</td>\n",
       "      <td>Front-end developer</td>\n",
       "      <td>Moderately satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71527</th>\n",
       "      <td>101588</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>India</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>500 to 999 employees</td>\n",
       "      <td>Back-end developer</td>\n",
       "      <td>Extremely satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71528</th>\n",
       "      <td>101589</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>A business discipline (ex. accounting, finance...</td>\n",
       "      <td>Fewer than 10 employees</td>\n",
       "      <td>Back-end developer;C-suite executive (CEO, CTO...</td>\n",
       "      <td>Extremely satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71529</th>\n",
       "      <td>101591</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Another engineering discipline (ex. civil, ele...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>Mobile developer</td>\n",
       "      <td>Extremely satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71530</th>\n",
       "      <td>101592</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>Full-stack developer</td>\n",
       "      <td>Extremely dissatisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21210 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Respondent Hobby OpenSource        Country         Student  \\\n",
       "2               4   Yes        Yes  United States              No   \n",
       "3               5    No         No  United States              No   \n",
       "4               7   Yes         No   South Africa  Yes, part-time   \n",
       "6               9   Yes        Yes  United States              No   \n",
       "8              11   Yes        Yes  United States              No   \n",
       "...           ...   ...        ...            ...             ...   \n",
       "71525      101585   Yes         No          India              No   \n",
       "71527      101588   Yes         No          India  Yes, full-time   \n",
       "71528      101589   Yes         No  United States              No   \n",
       "71529      101591   Yes        Yes  United States              No   \n",
       "71530      101592   Yes         No  United States              No   \n",
       "\n",
       "               Employment                                    FormalEducation  \\\n",
       "2      Employed full-time                                   Associate degree   \n",
       "3      Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "4      Employed full-time  Some college/university study without earning ...   \n",
       "6      Employed full-time  Some college/university study without earning ...   \n",
       "8      Employed full-time  Some college/university study without earning ...   \n",
       "...                   ...                                                ...   \n",
       "71525  Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "71527  Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "71528  Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "71529  Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "71530  Employed full-time        Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
       "\n",
       "                                          UndergradMajor  \\\n",
       "2      Computer science, computer engineering, or sof...   \n",
       "3      Computer science, computer engineering, or sof...   \n",
       "4      Computer science, computer engineering, or sof...   \n",
       "6      Computer science, computer engineering, or sof...   \n",
       "8      Fine arts or performing arts (ex. graphic desi...   \n",
       "...                                                  ...   \n",
       "71525  Computer science, computer engineering, or sof...   \n",
       "71527  Computer science, computer engineering, or sof...   \n",
       "71528  A business discipline (ex. accounting, finance...   \n",
       "71529  Another engineering discipline (ex. civil, ele...   \n",
       "71530  Computer science, computer engineering, or sof...   \n",
       "\n",
       "                    CompanySize  \\\n",
       "2            20 to 99 employees   \n",
       "3          100 to 499 employees   \n",
       "4      10,000 or more employees   \n",
       "6      10,000 or more employees   \n",
       "8          100 to 499 employees   \n",
       "...                         ...   \n",
       "71525  1,000 to 4,999 employees   \n",
       "71527      500 to 999 employees   \n",
       "71528   Fewer than 10 employees   \n",
       "71529  10,000 or more employees   \n",
       "71530  10,000 or more employees   \n",
       "\n",
       "                                                 DevType  \\\n",
       "2               Engineering manager;Full-stack developer   \n",
       "3                                   Full-stack developer   \n",
       "4      Data or business analyst;Desktop or enterprise...   \n",
       "6      Back-end developer;Front-end developer;Full-st...   \n",
       "8      Back-end developer;C-suite executive (CEO, CTO...   \n",
       "...                                                  ...   \n",
       "71525                                Front-end developer   \n",
       "71527                                 Back-end developer   \n",
       "71528  Back-end developer;C-suite executive (CEO, CTO...   \n",
       "71529                                   Mobile developer   \n",
       "71530                               Full-stack developer   \n",
       "\n",
       "                          JobSatisfaction  \n",
       "2                    Moderately satisfied  \n",
       "3      Neither satisfied nor dissatisfied  \n",
       "4                      Slightly satisfied  \n",
       "6                      Slightly satisfied  \n",
       "8                    Moderately satisfied  \n",
       "...                                   ...  \n",
       "71525                Moderately satisfied  \n",
       "71527                 Extremely satisfied  \n",
       "71528                 Extremely satisfied  \n",
       "71529                 Extremely satisfied  \n",
       "71530              Extremely dissatisfied  \n",
       "\n",
       "[21210 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.query('Country == \"India\" or Country==\"United States\" or Country == \"Italy\" or Country==\"South Africa\"')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('CompanySize', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Respondent</th>\n",
       "      <th>Hobby</th>\n",
       "      <th>OpenSource</th>\n",
       "      <th>Country</th>\n",
       "      <th>Student</th>\n",
       "      <th>Employment</th>\n",
       "      <th>FormalEducation</th>\n",
       "      <th>UndergradMajor</th>\n",
       "      <th>DevType</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Associate degree</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Engineering manager;Full-stack developer</td>\n",
       "      <td>Moderately satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Full-stack developer</td>\n",
       "      <td>Neither satisfied nor dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Yes, part-time</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Data or business analyst;Desktop or enterprise...</td>\n",
       "      <td>Slightly satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Back-end developer;Front-end developer;Full-st...</td>\n",
       "      <td>Slightly satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Fine arts or performing arts (ex. graphic desi...</td>\n",
       "      <td>Back-end developer;C-suite executive (CEO, CTO...</td>\n",
       "      <td>Moderately satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71525</th>\n",
       "      <td>101585</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>India</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Front-end developer</td>\n",
       "      <td>Moderately satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71527</th>\n",
       "      <td>101588</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>India</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Back-end developer</td>\n",
       "      <td>Extremely satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71528</th>\n",
       "      <td>101589</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>A business discipline (ex. accounting, finance...</td>\n",
       "      <td>Back-end developer;C-suite executive (CEO, CTO...</td>\n",
       "      <td>Extremely satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71529</th>\n",
       "      <td>101591</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Another engineering discipline (ex. civil, ele...</td>\n",
       "      <td>Mobile developer</td>\n",
       "      <td>Extremely satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71530</th>\n",
       "      <td>101592</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Master</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Full-stack developer</td>\n",
       "      <td>Extremely dissatisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21210 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Respondent Hobby OpenSource        Country         Student  \\\n",
       "2               4   Yes        Yes  United States              No   \n",
       "3               5    No         No  United States              No   \n",
       "4               7   Yes         No   South Africa  Yes, part-time   \n",
       "6               9   Yes        Yes  United States              No   \n",
       "8              11   Yes        Yes  United States              No   \n",
       "...           ...   ...        ...            ...             ...   \n",
       "71525      101585   Yes         No          India              No   \n",
       "71527      101588   Yes         No          India  Yes, full-time   \n",
       "71528      101589   Yes         No  United States              No   \n",
       "71529      101591   Yes        Yes  United States              No   \n",
       "71530      101592   Yes         No  United States              No   \n",
       "\n",
       "               Employment                                    FormalEducation  \\\n",
       "2      Employed full-time                                   Associate degree   \n",
       "3      Employed full-time                                           Bachelor   \n",
       "4      Employed full-time  Some college/university study without earning ...   \n",
       "6      Employed full-time  Some college/university study without earning ...   \n",
       "8      Employed full-time  Some college/university study without earning ...   \n",
       "...                   ...                                                ...   \n",
       "71525  Employed full-time                                           Bachelor   \n",
       "71527  Employed full-time                                           Bachelor   \n",
       "71528  Employed full-time                                           Bachelor   \n",
       "71529  Employed full-time                                           Bachelor   \n",
       "71530  Employed full-time                                             Master   \n",
       "\n",
       "                                          UndergradMajor  \\\n",
       "2      Computer science, computer engineering, or sof...   \n",
       "3      Computer science, computer engineering, or sof...   \n",
       "4      Computer science, computer engineering, or sof...   \n",
       "6      Computer science, computer engineering, or sof...   \n",
       "8      Fine arts or performing arts (ex. graphic desi...   \n",
       "...                                                  ...   \n",
       "71525  Computer science, computer engineering, or sof...   \n",
       "71527  Computer science, computer engineering, or sof...   \n",
       "71528  A business discipline (ex. accounting, finance...   \n",
       "71529  Another engineering discipline (ex. civil, ele...   \n",
       "71530  Computer science, computer engineering, or sof...   \n",
       "\n",
       "                                                 DevType  \\\n",
       "2               Engineering manager;Full-stack developer   \n",
       "3                                   Full-stack developer   \n",
       "4      Data or business analyst;Desktop or enterprise...   \n",
       "6      Back-end developer;Front-end developer;Full-st...   \n",
       "8      Back-end developer;C-suite executive (CEO, CTO...   \n",
       "...                                                  ...   \n",
       "71525                                Front-end developer   \n",
       "71527                                 Back-end developer   \n",
       "71528  Back-end developer;C-suite executive (CEO, CTO...   \n",
       "71529                                   Mobile developer   \n",
       "71530                               Full-stack developer   \n",
       "\n",
       "                          JobSatisfaction  \n",
       "2                    Moderately satisfied  \n",
       "3      Neither satisfied nor dissatisfied  \n",
       "4                      Slightly satisfied  \n",
       "6                      Slightly satisfied  \n",
       "8                    Moderately satisfied  \n",
       "...                                   ...  \n",
       "71525                Moderately satisfied  \n",
       "71527                 Extremely satisfied  \n",
       "71528                 Extremely satisfied  \n",
       "71529                 Extremely satisfied  \n",
       "71530              Extremely dissatisfied  \n",
       "\n",
       "[21210 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.replace('Bachelor’s degree (BA, BS, B.Eng., etc.)',\"Bachelor\")\n",
    "df = df.replace('Master’s degree (MA, MS, M.Eng., MBA, etc.)',\"Master\")\n",
    "df = df.replace('Other doctoral degree (Ph.D, Ed.D., etc.)',\"Ph.D\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hobby</th>\n",
       "      <th>OpenSource</th>\n",
       "      <th>Country</th>\n",
       "      <th>Employment</th>\n",
       "      <th>FormalEducation</th>\n",
       "      <th>UndergradMajor</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Neither satisfied nor dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>India</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Another engineering discipline (ex. civil, ele...</td>\n",
       "      <td>Extremely satisfied</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>India</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Master</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Moderately satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>A social science (ex. anthropology, psychology...</td>\n",
       "      <td>Moderately satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Extremely satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71525</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>India</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Moderately satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71527</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>India</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Extremely satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71528</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>A business discipline (ex. accounting, finance...</td>\n",
       "      <td>Extremely satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71529</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Another engineering discipline (ex. civil, ele...</td>\n",
       "      <td>Extremely satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71530</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Master</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>Extremely dissatisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18389 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Hobby OpenSource        Country          Employment FormalEducation  \\\n",
       "3        No         No  United States  Employed full-time        Bachelor   \n",
       "13       No         No          India  Employed full-time        Bachelor   \n",
       "24      Yes         No          India  Employed full-time          Master   \n",
       "27       No         No  United States  Employed full-time        Bachelor   \n",
       "28      Yes        Yes  United States  Employed full-time        Bachelor   \n",
       "...     ...        ...            ...                 ...             ...   \n",
       "71525   Yes         No          India  Employed full-time        Bachelor   \n",
       "71527   Yes         No          India  Employed full-time        Bachelor   \n",
       "71528   Yes         No  United States  Employed full-time        Bachelor   \n",
       "71529   Yes        Yes  United States  Employed full-time        Bachelor   \n",
       "71530   Yes         No  United States  Employed full-time          Master   \n",
       "\n",
       "                                          UndergradMajor  \\\n",
       "3      Computer science, computer engineering, or sof...   \n",
       "13     Another engineering discipline (ex. civil, ele...   \n",
       "24     Computer science, computer engineering, or sof...   \n",
       "27     A social science (ex. anthropology, psychology...   \n",
       "28     Computer science, computer engineering, or sof...   \n",
       "...                                                  ...   \n",
       "71525  Computer science, computer engineering, or sof...   \n",
       "71527  Computer science, computer engineering, or sof...   \n",
       "71528  A business discipline (ex. accounting, finance...   \n",
       "71529  Another engineering discipline (ex. civil, ele...   \n",
       "71530  Computer science, computer engineering, or sof...   \n",
       "\n",
       "                          JobSatisfaction  \n",
       "3      Neither satisfied nor dissatisfied  \n",
       "13                    Extremely satisfied  \n",
       "24                   Moderately satisfied  \n",
       "27                   Moderately satisfied  \n",
       "28                    Extremely satisfied  \n",
       "...                                   ...  \n",
       "71525                Moderately satisfied  \n",
       "71527                 Extremely satisfied  \n",
       "71528                 Extremely satisfied  \n",
       "71529                 Extremely satisfied  \n",
       "71530              Extremely dissatisfied  \n",
       "\n",
       "[18389 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.query(\"FormalEducation == 'Ph.D' or FormalEducation == 'Bachelor' or FormalEducation=='Master'\")\n",
    "df = df.drop('Student', axis=1)\n",
    "df = df.drop('DevType', axis=1)\n",
    "df = df.drop('Respondent', axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_values():\n",
    "    for n in df.columns:\n",
    "        print(n)\n",
    "        print(df[n].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bq09W7ec/u/f/3PglvgYAAACAA7vhiXaYmW+rHl5rvX9mzl3rCc3MXdVdVWfOnOnk5ORaf8vr4vz586fmWDidrFGOnTXKsbNGOXbWKJ/P61/46BPvdB2cefbxzOXQ/Lwep2fC79InDEXVN1bfPjOvrL60+pvVT1bPmZkb9llDt1QP7f0fqp5fPTgzN1RfVv3hReOPufhrPmetdU91T9XZs2fXuXPnvoDDOj4nJyedlmPhdLJGOXbWKMfOGuXYWaN8Pq+5+12HnkJ1IRL9+Aev5J+pp9/Hv/vcoafAJTwTfpc+4aVna60fXmvdsta6tQtvRv3La63vrn6l+o69253VO/b2O/fj9vO/vNZae/zV+65oL6huq379qh0JAAAAAE/JU0m1/1f1lpn5R9VvVW/a42+q/sXMPFA90oW41FrrQzPz1urD1aPV69Zaf/EUvj8AAAAAV9GTCkVrrZPqZG9/rEvctWyt9V+rv3eZr/+x6see7CQBAAAAuPau9K5nAAAAAJxyQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAVd1w6AkAAAAAf9Wtd7/r0FPgEn729hsPPYVrzhlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsAlFAAAAAFRCEQAAAACbUAQAAABAJRQBAAAAsD1hKJqZL52ZX5+Z/zAzH5qZf7jHXzAzvzYzD8zMv5qZL9njf20/fmA/f+tFr/XDe/z3Zubl1+yoAAAAAHjSruSMoj+rvnmt9TXV11a3z8xLqn9c/cRa629Vn6leu/d/bfWZPf4Te79m5qurV1d/p7q9+mcz86yreCwAAAAAPAVPGIrWBef3wy/eH6v65upte/y+6lV7+479uP38S2dm9vhb1lp/ttb6T9UD1YuvxkEAAAAA8NRd0XsUzcyzZuYD1cPV/dV/rP5orfXo3uXB6ua9fXP1yar9/GerL794/BJfAwAAAMCB3XAlO621/qL62pl5TvX26m9fqwnNzF3VXVVnzpzp5OTkWn2r6+r8+fOn5lg4naxRjp01yrGzRjl21iifz+tf+OgT73QdnHn28cwFLuWZ8Lv0ikLRY9ZafzQzv1J9Q/WcmblhnzV0S/XQ3u2h6vnVgzNzQ/Vl1R9eNP6Yi7/m4u9xT3VP1dmzZ9e5c+ee1AEdq5OTk07LsXA6WaMcO2uUY2eNcuysUT6f19z9rkNPoboQiX78g0/qn6lwXf3s7Tee+t+lV3LXs6/cZxI1M8+uvqX6SPUr1Xfs3e6s3rG337kft5//5bXW2uOv3ndFe0F1W/XrV+k4AAAAAHiKriTV3lTdt+9Q9kXVW9davzgzH67eMjP/qPqt6k17/zdV/2JmHqge6cKdzlprfWhm3lp9uHq0et2+pA0AAACAI/CEoWit9dvV111i/GNd4q5la63/Wv29y7zWj1U/9uSnCQAAAMC1dkV3PQMAAADg9BOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKjqhkNPAAAArpVb737XoadwNF7/wkd7zRH8eXz8jd966CkA8Hk4owgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACASigCAAAAYBOKAAAAAKiEIgAAAAA2oQgAAACA6gpC0cw8f2Z+ZWY+PDMfmpkf3OPPm5n7Z+aj+/Nz9/jMzE/NzAMz89sz86KLXuvOvf9HZ+bOa3dYAAAAADxZV3JG0aPV69daX129pHrdzHx1dXf13rXWbdV79+OqV1S37Y+7qp+uC2GpekP19dWLqzc8FpcAAAAAOLwnDEVrrU+ttX5zb/+X6iPVzdUd1X17t/uqV+3tO6o3rwt+tXrOzNxUvby6f631yFrrM9X91e1X82AAAAAA+MI9qfcomplbq6+rfq06s9b61H7q09WZvX1z9cmLvuzBPXa5cQAAAACOwA1XuuPM/PXqF6ofWmv98cx87rm11pqZdTUmNDN3deGStc6cOdPJycnVeNmDO3/+/Kk5Fk4na5RjZ41y7KzR4/T6Fz566CkcjTPPPo4/Dz8nx+kY1kYdzzqFy3km/H1/RaFoZr64C5Ho59Za/3oP//7M3LTW+tS+tOzhPf5Q9fyLvvyWPfZQde5x4yeP/15rrXuqe6rOnj27zp079/hdnpZOTk46LcfC6WSNcuysUY6dNXqcXnP3uw49haPx+hc+2o9/8Ir/P/E18/HvPnfoKXAJx/KzcizrFC7nZ2+/8dT/fX8ldz2b6k3VR9Za/+Sip95ZPXbnsjurd1w0/r377mcvqT67L1F7T/WymXnufhPrl+0xAAAAAI7AlaTab6y+p/rgzHxgj/2D6o3VW2fmtdUnqu/cz727emX1QPWn1fdVrbUemZkfrd639/uRtdYjV+MgAAAAAHjqnjAUrbX+fTWXefqll9h/Va+7zGvdW937ZCYIwGHdekSnoh/LafHH4ONv/NZDTwEAgFPoSd31DAAAAIDTSygCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoBKKAAAAANiEIgAAAAAqoQgAAACATSgCAAAAoKobDj0BOKRb737XoadwNF7/wkd7zRH8eXz8jd966CkAAAA8YzmjCAAAAIBKKAIAAABgE4oAAAAAqIQiAAAAADahCAAAAIBKKAIAAABgE4oAAAAAqIQiAAAAADahCAAAAIBKKAIAAABgE4oAAAAAqIQiAAAAADahCAAAAIBKKAIAAABgE4oAAAAAqIQiAAAAADahCAAAAIBKKAIAAABgE4oAAAAAqIQiAAAAADahCAAAAIBKKAIAAABgE4oAAAAAqIQiAAAAADahCAAAAIBKKAIAAABgE4oAAAAAqIQiAAAAADahCAAAAIBKKAIAAABgE4oAAAAAqOqGQ08AAOA0+OBDn+01d7/r0NMAAHhKnFEEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAAm1AEAAAAQCUUAQAAALAJRQAAAABUQhEAAAAA2xOGopm5d2YenpnfuWjseTNz/8x8dH9+7h6fmfmpmXlgZn57Zl500dfcuff/6MzceW0OBwAAAIAv1JWcUfSz1e2PG7u7eu9a67bqvftx1Suq2/bHXdVP14WwVL2h+vrqxdUbHotLAAAAAByHJwxFa61/Wz3yuOE7qvv29n3Vqy4af/O64Fer58zMTdXLq/vXWo+stT5T3d9/H58AAAAAOKAv9D2Kzqy1PrW3P12d2ds3V5+8aL8H99jlxgEAAAA4Ejc81RdYa62ZWVdjMlUzc1cXLlvrzJkznZycXK2XPqjz58+fmmM5TV7/wkcPPYWjcebZx/Hn4efk+BzDuqjjWaPHws/K8bFGOXbHskb9/jpOx7A26njWKVzOM+Hf9l9oKPr9mblprfWpfWnZw3v8oer5F+13yx57qDr3uPGTS73wWuue6p6qs2fPrnPnzl1qt6edk5OTTsuxnCavuftdh57C0Xj9Cx/txz/4lNvxU/bx7z536CnwOMfyc3Isa/RY+Fk5Pv/3z73DGuWoHcvvUb+/jpO/7+HK/OztN576f9t/oZeevbN67M5ld1bvuGj8e/fdz15SfXZfovae6mUz89z9JtYv22MAAAAAHIknTLUz8/NdOBvoK2bmwS7cveyN1Vtn5rXVJ6rv3Lu/u3pl9UD1p9X3Va21HpmZH63et/f7kbXW498gGwAAAIADesJQtNb6rss89dJL7Luq113mde6t7n1SswMAAADguvlCLz0DAAAA4JQRigAAAACohCIAAAAANqEIAAAAgEooAgAAAGATigAAAACohCIAAAAANqEIAAAAgEooAgAAAGATigAAAACohCIAAAAANqEIAAAAgEooAgAAAGATigAAAACohCIAAAAANqEIAAAAgEooAgAAAGATigAAAACohCIAAAAANqEIAAAAgEooAgAAAGC74dATeKb44EOf7TV3v+vQ0wAAAAC4LGcUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABsQhEAAAAAlVAEAAAAwCYUAQAAAFAJRQAAAABs1z0UzcztM/N7M/PAzNx9vb8/AAAAAJd2XUPRzDyr+n+qV1RfXX3XzHz19ZwDAAAAAJd2vc8oenH1wFrrY2utP6/eUt1xnecAAAAAwCVc71B0c/XJix4/uMcAAAAAOLBZa12/bzbzHdXta63/fT/+nurr11rff9E+d1V37YdfVf3edZvgtfUV1R8cehLweVijHDtrlGNnjXLsrFGeDqxTjt1pWaP/01rrKy/1xA3XeSIPVc+/6PEte+xz1lr3VPdcz0ldDzPzG2uts4eeB1yONcqxs0Y5dtYox84a5enAOuXYPRPW6PW+9Ox91W0z84KZ+ZLq1dU7r/McAAAAALiE63pG0Vrr0Zn5/uo91bOqe9daH7qecwAAAADg0q73pWettd5dvft6f98jcOoup+PUsUY5dtYox84a5dhZozwdWKccu1O/Rq/rm1kDAAAAcLyu93sUAQAAAHCkhKLrYGZun5nfm5kHZubuQ88HLjYz987MwzPzO4eeC1zKzDx/Zn5lZj48Mx+amR889JzgYjPzpTPz6zPzH/Ya/YeHnhNcysw8a2Z+a2Z+8dBzgcebmY/PzAdn5gMz8xuHng883sw8Z2beNjO/OzMfmZlvOPScrhWXnl1jM/Os6v+rvqV6sAt3fvuutdaHDzox2Gbmm6rz1ZvXWv/zoecDjzczN1U3rbV+c2b+RvX+6lV+j3IsZmaqG9da52fmi6t/X/3gWutXDzw1+Ctm5v+szlZ/c631bYeeD1xsZj5enV1r/cGh5wKXMjP3Vf9urfUz+y7u/8Na648OPK1rwhlF196LqwfWWh9ba/159ZbqjgPPCT5nrfVvq0cOPQ+4nLXWp9Zav7m3/0v1kermw84K/tK64Px++MX7w/+J46jMzC3Vt1Y/c+i5ADzdzMyXVd9UvalqrfXnpzUSlVB0PdxcffKixw/mHzgAX5CZubX6uurXDjwV+Cv2JT0fqB6u7l9rWaMcm39a/f3qvx14HnA5q/o3M/P+mbnr0JOBx3lB9Z+rf74v4f2Zmbnx0JO6VoQiAJ4WZuavV79Q/dBa648PPR+42FrrL9ZaX1vdUr14ZlzKy9GYmW+rHl5rvf/Qc4HP4++utV5UvaJ63X57BDgWN1Qvqn56rfV11Z9Up/b9h4Wia++h6vkXPb5ljwFwhfb7vvxC9XNrrX996PnA5ezT0H+luv3AU4GLfWP17fs9YN5SffPM/L+HnRL8VWuth/bnh6u3d+EtPOBYPFg9eNEZw2/rQjg6lYSia+991W0z84L9hlevrt554DkBPG3sNwp+U/WRtdY/OfR84PFm5itn5jl7+9lduIHF7x50UnCRtdYPr7VuWWvd2oX/Fv3ltdb/duBpwefMzI37hhXty3leVrkjL0djrfXp6pMz81V76KXVqb2xyg2HnsBpt9Z6dGa+v3pP9azq3rXWhw48Lficmfn56lz1FTPzYPWGtdabDjsr+Cu+sfqe6oP7PWCq/sFa692HmxL8FTdV9+07nX5R9da1ltuPA1y5M9XbL/y/oW6o/uVa65cOOyX47/xA9XP7BJCPVd934PlcM7OWm3IAAAAA4NIzAAAAADahCAAAAIBKKAIAAABgE4oAAAAAqIQiAAAAADahCAAAAIBKKAIAAABgE4oAAAAAqOr/BwgIQ3Elr2JwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "js_dict = {\n",
    "    'Extremely satisfied':6,\n",
    "    'Moderately satisfied':5,\n",
    "    'Slightly satisfied':4,\n",
    "    'Neither satisfied nor dissatisfied':3, \n",
    "    'Slightly dissatisfied':2,\n",
    "    'Moderately dissatisfied':1,\n",
    "    'Extremely dissatisfied':0\n",
    "}\n",
    "\n",
    "plt.figure(figsize=[20,10])\n",
    "df['JobSatisfaction'] = df['JobSatisfaction'].map(js_dict)\n",
    "df['JobSatisfaction'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace Undergrad major values with Stem/non-stem. Delete 'i never Declared a major'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hobby\n",
      "['No' 'Yes']\n",
      "OpenSource\n",
      "['No' 'Yes']\n",
      "Country\n",
      "['United States' 'India' 'South Africa' 'Italy']\n",
      "Employment\n",
      "['Employed full-time' 'Employed part-time']\n",
      "FormalEducation\n",
      "['Bachelor' 'Master' 'Ph.D']\n",
      "UndergradMajor\n",
      "['Computer science, computer engineering, or software engineering'\n",
      " 'Another engineering discipline (ex. civil, electrical, mechanical)'\n",
      " 'A social science (ex. anthropology, psychology, political science)'\n",
      " 'Web development or web design'\n",
      " 'A natural science (ex. biology, chemistry, physics)'\n",
      " 'Fine arts or performing arts (ex. graphic design, music, studio art)'\n",
      " 'A humanities discipline (ex. literature, history, philosophy)'\n",
      " 'A business discipline (ex. accounting, finance, marketing)'\n",
      " 'Mathematics or statistics'\n",
      " 'Information systems, information technology, or system administration'\n",
      " 'A health science (ex. nursing, pharmacy, radiology)'\n",
      " 'I never declared a major']\n",
      "JobSatisfaction\n",
      "[3 6 5 0 1 2 4]\n"
     ]
    }
   ],
   "source": [
    "check_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='FormalEducation', ylabel='Country'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = df.query('JobSatisfaction==\"Neither satisfied nor dissatisfied\"')\n",
    "# a = a.groupby('Country')['Respondent'].count()\n",
    "# b = df.groupby('Country')['Respondent'].count()\n",
    "# a/b\n",
    "# df.query('Country==\"Italy\"').shape\n",
    "# df.shape\n",
    "df.query('Employment ==\"Employed full-time\"').shape[0]/df.shape[0]\n",
    "# df = df[df['JobSatisfaction']!=\"Neither satisfied nor dissatisfied\"]\n",
    "\n",
    "contigency_pct = pd.crosstab(df['Country'], df['FormalEducation'], normalize='all')\n",
    "contigency_pct\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(contigency_pct, annot=True, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999999746792149"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c, p, dof, expected = chi2_contingency(contigency_pct)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hobby Hobby : 0.0\n",
      "Hobby OpenSource : 1.8943523288807258e-208\n",
      "Hobby Country : 0.0015202187330951903\n",
      "Hobby Employment : 0.4710185002775018\n",
      "Hobby FormalEducation : 0.34835242768931063\n",
      "Hobby UndergradMajor : 0.005540542433909045\n",
      "Hobby JobSatisfaction : 0.0014587793432692122\n",
      "OpenSource Hobby : 1.8943523288808334e-208\n",
      "OpenSource OpenSource : 0.0\n",
      "OpenSource Country : 5.353459971786939e-16\n",
      "OpenSource Employment : 0.06596313185791074\n",
      "OpenSource FormalEducation : 2.3950709782153245e-08\n",
      "OpenSource UndergradMajor : 1.7620231301004023e-05\n",
      "OpenSource JobSatisfaction : 5.308322788119514e-10\n",
      "Country Hobby : 0.0015202187330951912\n",
      "Country OpenSource : 5.353459971786939e-16\n",
      "Country Country : 0.0\n",
      "Country Employment : 0.0006882405532031674\n",
      "Country FormalEducation : 1.9136126083317547e-88\n",
      "Country UndergradMajor : 0.0\n",
      "Country JobSatisfaction : 3.4605770643860615e-153\n",
      "Employment Hobby : 0.4710185002775018\n",
      "Employment OpenSource : 0.06596313185791074\n",
      "Employment Country : 0.0006882405532031674\n",
      "Employment Employment : 0.0\n",
      "Employment FormalEducation : 0.29959906343740855\n",
      "Employment UndergradMajor : 0.008077552050914724\n",
      "Employment JobSatisfaction : 0.00023442350051910187\n",
      "FormalEducation Hobby : 0.34835242768931074\n",
      "FormalEducation OpenSource : 2.3950709782153245e-08\n",
      "FormalEducation Country : 1.9136126083317547e-88\n",
      "FormalEducation Employment : 0.29959906343740855\n",
      "FormalEducation FormalEducation : 0.0\n",
      "FormalEducation UndergradMajor : 8.44705622596076e-267\n",
      "FormalEducation JobSatisfaction : 8.1808709815137e-09\n",
      "UndergradMajor Hobby : 0.005540542433909045\n",
      "UndergradMajor OpenSource : 1.7620231301004023e-05\n",
      "UndergradMajor Country : 0.0\n",
      "UndergradMajor Employment : 0.008077552050914724\n",
      "UndergradMajor FormalEducation : 8.44705622596076e-267\n",
      "UndergradMajor UndergradMajor : 0.0\n",
      "UndergradMajor JobSatisfaction : 5.810305043425125e-14\n",
      "JobSatisfaction Hobby : 0.0014587793432692122\n",
      "JobSatisfaction OpenSource : 5.308322788119514e-10\n",
      "JobSatisfaction Country : 3.460577064385666e-153\n",
      "JobSatisfaction Employment : 0.00023442350051910187\n",
      "JobSatisfaction FormalEducation : 8.1808709815137e-09\n",
      "JobSatisfaction UndergradMajor : 5.81030504342521e-14\n",
      "JobSatisfaction JobSatisfaction : 0.0\n"
     ]
    }
   ],
   "source": [
    "a = 0\n",
    "b = 0\n",
    "mat=np.zeros([7,7])\n",
    "for i in df.columns:\n",
    "    for j in df.columns:\n",
    "        contigency_pct = pd.crosstab(df[i], df[j])\n",
    "        c, p, dof, expected = chi2_contingency(contigency_pct)\n",
    "        print(i,j,\":\",p)\n",
    "        mat[a][b]=p\n",
    "        b=b+1\n",
    "    a = a+1\n",
    "    b=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(mat, annot=True, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " (>=0.05 Independent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# alpha = 0.0005 #should be 0.05, used to prune even more dependent values so that the graph has less edges\n",
    "alpha = 5e-7\n",
    "sns.heatmap((lambda x: x>=alpha)(mat), annot=True, cmap=\"YlGnBu\")\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "8d3942d9d0df7904cc5632f5715cdb07af9dfb8cdd44fc884c5ed30e27224bf8"
  },
  "kernelspec": {
   "display_name": "Python 3.7.1 32-bit",
   "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": "3.7.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}