Cleaning_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from scipy import stats\n",
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Training_set.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ASECWT</th>\n",
       "      <th>RELATE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>RACE</th>\n",
       "      <th>MARST</th>\n",
       "      <th>POPSTAT</th>\n",
       "      <th>VETSTAT</th>\n",
       "      <th>FAMREL</th>\n",
       "      <th>BPL</th>\n",
       "      <th>YRIMMIG</th>\n",
       "      <th>OCC2010</th>\n",
       "      <th>IND1990</th>\n",
       "      <th>CLASSWKR</th>\n",
       "      <th>UHRSWORK1</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>EDUC99</th>\n",
       "      <th>DIFFANY</th>\n",
       "      <th>INCTOT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1316.02</td>\n",
       "      <td>101</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9900</td>\n",
       "      <td>0</td>\n",
       "      <td>9920</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>999</td>\n",
       "      <td>92</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>50202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>824.29</td>\n",
       "      <td>101</td>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9900</td>\n",
       "      <td>0</td>\n",
       "      <td>5550</td>\n",
       "      <td>412</td>\n",
       "      <td>25</td>\n",
       "      <td>997</td>\n",
       "      <td>91</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>19201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>824.29</td>\n",
       "      <td>201</td>\n",
       "      <td>55</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9900</td>\n",
       "      <td>0</td>\n",
       "      <td>420</td>\n",
       "      <td>850</td>\n",
       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "      <td>123</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>121684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>731.67</td>\n",
       "      <td>301</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9900</td>\n",
       "      <td>0</td>\n",
       "      <td>9920</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>999999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1106.47</td>\n",
       "      <td>301</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9900</td>\n",
       "      <td>0</td>\n",
       "      <td>9920</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>999999999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ASECWT  RELATE  AGE  SEX  RACE  MARST  POPSTAT  VETSTAT  FAMREL   BPL  \\\n",
       "0  1316.02     101   73    1   100      6        1        2       0  9900   \n",
       "1   824.29     101   48    1   100      1        1        1       1  9900   \n",
       "2   824.29     201   55    2   100      1        1        1       2  9900   \n",
       "3   731.67     301   12    1   100      6        3        0       3  9900   \n",
       "4  1106.47     301    6    2   200      6        3        0       3  9900   \n",
       "\n",
       "   YRIMMIG  OCC2010  IND1990  CLASSWKR  UHRSWORK1  EDUC  EDUC99  DIFFANY  \\\n",
       "0        0     9920        0         0        999    92      14        2   \n",
       "1        0     5550      412        25        997    91      13        1   \n",
       "2        0      420      850        21         40   123      16        1   \n",
       "3        0     9920        0         0        999     1       0        0   \n",
       "4        0     9920        0         0        999     1       0        0   \n",
       "\n",
       "      INCTOT  \n",
       "0      50202  \n",
       "1      19201  \n",
       "2     121684  \n",
       "3  999999999  \n",
       "4  999999999  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(144000, 19)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MAKE TARGET VARIABLE BINARY\n",
    "df['INCTOT'] = np.where(df['INCTOT'] > 63179, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    90065\n",
       "1    53935\n",
       "Name: INCTOT, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.INCTOT.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA CLEANING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RECODING CATEGORICAL VARIABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RELATIONSHIP TO HOUSEHOLD HEAD\n",
    "df['RELATE'].replace({101:'head', \n",
    "                    201:'spouse',  \n",
    "                    301:'child', \n",
    "                    1114:'unmarried partner',\n",
    "                    901:'grandchild', \n",
    "                    501:'parent', \n",
    "                    1001:'other relative',\n",
    "                    1115:'roommate', \n",
    "                    701:'sibling', \n",
    "                    1260:'other nonrelative',\n",
    "                    1241:'lodger', \n",
    "                    1242:'foster children'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CONSOLIDATE VALUES\n",
    "df['RELATE'].replace({'lodger':'roommate', \n",
    "                    'unmarried':'spouse',  \n",
    "                    'foster children':'child'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17    2379\n",
       "85    2352\n",
       "10    2336\n",
       "11    2288\n",
       "80    2286\n",
       "      ... \n",
       "75     893\n",
       "76     786\n",
       "77     731\n",
       "78     639\n",
       "79     586\n",
       "Name: AGE, Length: 82, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.AGE.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    74380\n",
       "1    69620\n",
       "Name: SEX, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.SEX.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RECODE RACE\n",
    "df['RACE'].replace({100:'white', \n",
    "                    200:'african-american',  \n",
    "                    651:'asian', \n",
    "                    300:'eskimo',\n",
    "                    802:'white-american-indian', \n",
    "                    801:'white-african-american', \n",
    "                    652:'hawaiian',\n",
    "                    803:'white-asian', \n",
    "                    805:'african-american-american-indian', \n",
    "                    810:'white-african-american-american-indian',\n",
    "                    804:'white-hawaiian', \n",
    "                    809:'asian-hawaiian',\n",
    "     \n",
    "                    813:'white-asian-hawaiian',  \n",
    "                    806:'african-american-asian', \n",
    "                    812:'white-american-indian-asian',\n",
    "                    830:'four-or-five-races', \n",
    "                    807:'african-american-hawaiian', \n",
    "                    811:'white-african-american-asian',\n",
    "                    816:'white-african-american-hawaiian', \n",
    "                    808:'american-indian-asian', \n",
    "                    814:'white-african-american-american-indian-asian',\n",
    "                    820:'unspecified-two-or-three-races', \n",
    "                    818:'african-american-american-indian-asian',\n",
    "                    819:'white-american-indian-asian-hawaiian',\n",
    "                    815: 'american-indian-hawaiian'}, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CONSOLIDATE RACE COLUMN\n",
    "df['RACE'].replace({\n",
    "                    \"white-american-indian\":'mixed', \n",
    "                    \"white-african-american\":'mixed',  \n",
    "                    \"african-american-american-indian\":'mixed', \n",
    "                    \"white-african-american-american-indian\":'mixed',\n",
    "                    \"white-hawaiian\":'mixed', \n",
    "                    \"white-asian\":'mixed', \n",
    "                    \"asian-hawaiian\":'mixed',\n",
    "                    \"white-asian-hawaiian\":'mixed', \n",
    "                    \"african-american-asian\":'mixed', \n",
    "                    \"white-american-indian-asian\":'mixed',\n",
    "                    \"four-or-five-races\":'mixed', \n",
    "                    \"african-american-hawaiian\":'mixed',\n",
    "                    \"white-african-american-asian\":'mixed', \n",
    "                    \"white-african-american-hawaiian\":'mixed',\n",
    "                    \"white-african-american-american-indian-asian\":'mixed', \n",
    "                    \"american-indian-asian\":'mixed', \n",
    "                    \"unspecified-two-or-three-races\":'mixed',\n",
    "                    \"african-american-american-indian-asian\":'mixed',\n",
    "                    \"white-american-indian-asian-hawaiian\":'mixed', \n",
    "                    \"american-indian-hawaiian\":'mixed'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MARITAL STATUS\n",
    "df['MARST'].replace({6:'single', \n",
    "                    1:'married-spouse-present',  \n",
    "                    4:'divorced', \n",
    "                    5:'widowed',\n",
    "                    3:'separated', \n",
    "                    2:'married-spouse-absent'}, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CONSOLIDATE MARITAL STATUS\n",
    "df['MARST'].replace({'married-spouse-present':'married', \n",
    "                    'divorced':'single',  \n",
    "                    'widowed':'single', \n",
    "                    'separated':'single',\n",
    "                    'married-spouse-absent':'married'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MARST    INCTOT\n",
       "married  0         23943\n",
       "         1         14465\n",
       "single   0         23180\n",
       "         1          5223\n",
       "Name: INCTOT, dtype: int64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('MARST').INCTOT.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RECODE POPSTAT\n",
    "df['POPSTAT'].replace({1:'adult-civilian', \n",
    "                    3:'armed-forces',  \n",
    "                    2:'child'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RECODE VETSTAT\n",
    "df['VETSTAT'].replace({1:'no-service', \n",
    "                    0:'other',  \n",
    "                    2:'yes'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RELATION IN FAMILY\n",
    "df['FAMREL'].replace({3:'child', \n",
    "                    1:'reference-person',  \n",
    "                    2:'spouse', \n",
    "                    0:'not-family-member',\n",
    "                    4:'other-relative'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# BIRTHPLACE RECODE\n",
    "df['BPL'].replace({9900 : 'usa',\n",
    "                    20000 : 'central-america',\n",
    "                    52100 : 'india-southwest-asia',\n",
    "                    51500 : 'southeast-asia',\n",
    "                    50000 : 'east-asia',\n",
    "                    11000 : 'us-outlying-areas',\n",
    "                    21030 : 'central-america',\n",
    "                    51800  : 'southeast-asia' ,  \n",
    "                    25000  : 'caribbean',   \n",
    "                    26010  : 'caribbean',  \n",
    "                    21040  : 'central-america',   \n",
    "                    45300  : 'central-eastern-europe',   \n",
    "                    15000  : 'other-north-america',   \n",
    "                    50200  : 'east-asia',   \n",
    "                    30025  : 'south-america',   \n",
    "                    21050  : 'central-america',   \n",
    "                    50100  : 'east-asia',   \n",
    "                    26030  : 'caribbean',   \n",
    "                    26020  : 'caribbean',   \n",
    "                    30015  : 'south-america',   \n",
    "                    52140  : 'india-southwest-asia',   \n",
    "                    30050  : 'south-america',   \n",
    "                    41000  : 'uk-ireland',   \n",
    "                    30065  : 'south-america',   \n",
    "                    60099  : 'central-africa',   \n",
    "                    30030  : 'south-america',   \n",
    "                    46500  : 'baltic-states',   \n",
    "                    50040  : 'east-asia',   \n",
    "                    60031  : 'west-africa',   \n",
    "                    46530  : 'baltic-states',   \n",
    "                    41300  : 'uk-ireland',   \n",
    "                    45500  : 'central-eastern-europe',   \n",
    "                    60044  : 'east-africa',   \n",
    "                    53000  : 'middle-east',   \n",
    "                    43400  : 'southern-europe',   \n",
    "                    51300  : 'southeast-asia',   \n",
    "                    52110  : 'india-southwest-asia',   \n",
    "                    51700  : 'southeast-asia',   \n",
    "                    50220  : 'east-asia',   \n",
    "                    50010  : 'east-asia',   \n",
    "                    53200  : 'middle-east',   \n",
    "                    60012  : 'africa',   \n",
    "                    21060  : 'central-america',   \n",
    "                    26060  : 'caribbean',   \n",
    "                    96000  : 'australia-new-zealand',   \n",
    "                    43800  : 'southern-europe',   \n",
    "                    30040  : 'south-america',   \n",
    "                    30005  : 'south-america',   \n",
    "                    42100  : 'western-europe',   \n",
    "                    52130  : 'india-southwest-asia',   \n",
    "                    21070  : 'central-america',   \n",
    "                    30010  : 'south-america',   \n",
    "                    30020  : 'south-america',   \n",
    "                    52200  : 'india-southwest-asia',   \n",
    "                    45600  : 'central-eastern-europe',   \n",
    "                    60034  : 'west-africa',  \n",
    "                    52000  : 'india-southwest-asia',   \n",
    "                    59900  : 'middle-east',   \n",
    "                    53400  : 'middle-east',   \n",
    "                    43600  : 'southern-europe',   \n",
    "                    60094  : 'east-africa',   \n",
    "                    45720  : 'central-eastern-europe',   \n",
    "                    53700  : 'middle-east',   \n",
    "                    60045  : 'east-africa',   \n",
    "                    60023  : 'west-africa',   \n",
    "                    51200  : 'southeast-asia',   \n",
    "                    41400  : 'uk-ireland',   \n",
    "                    54200  : 'middle-east',   \n",
    "                    51100  : 'southeast-asia',   \n",
    "                    52120  : 'india-southwest-asia',   \n",
    "                    70010  : 'australia-new-zealand',   \n",
    "                    26054  : 'caribbean',   \n",
    "                    21020  : 'central-america',   \n",
    "                    42500  : 'western-europe',  \n",
    "                    45675  : 'central-eastern-europe',   \n",
    "                    43300  : 'southern-europe',   \n",
    "                    72000  : 'australia-new-zealand',   \n",
    "                    54400  : 'middle-east',   \n",
    "                    60050  : 'east-africa',   \n",
    "                    30060  : 'south-america',   \n",
    "                    10500  : 'us-outlying-areas',   \n",
    "                    60096  : 'central-africa',   \n",
    "                    54100  : 'middle-east',   \n",
    "                    54000  : 'middle-east',   \n",
    "                    11500  : 'us-outlying-areas',   \n",
    "                    60014  : 'africa',\n",
    "                    71024  : 'australia-new-zealand',   \n",
    "                    53500  : 'middle-east',   \n",
    "                    21010  : 'central-america',   \n",
    "                    30090  : 'south-america',   \n",
    "                    51400  : 'southeast-asia',   \n",
    "                    41100  : 'uk-ireland',   \n",
    "                    71023  : 'australia-new-zealand',   \n",
    "                    55100  : 'eurasia',   \n",
    "                    40500  : 'northern-europe',   \n",
    "                    45650  : 'central-eastern-europe',   \n",
    "                    60016  : 'africa',   \n",
    "                    71021  : 'australia-new-zealand',   \n",
    "                    60018  : 'africa',   \n",
    "                    45700  : 'central-eastern-europe',   \n",
    "                    49900  : 'baltic-states',   \n",
    "                    46540  : 'baltic-states',   \n",
    "                    60033  : 'west-africa', \n",
    "                    42000  : 'western-europe',   \n",
    "                    46590  : 'baltic-states',   \n",
    "                    51600  : 'southeast-asia',   \n",
    "                    60040  : 'east-africa',   \n",
    "                    55500  : 'central-asia',   \n",
    "                    55400  : 'central-asia',   \n",
    "                    60032  : 'west-africa',   \n",
    "                    26080  : 'caribbean',   \n",
    "                    52150  : 'india-southwest-asia',   \n",
    "                    26044  : 'caribbean',   \n",
    "                    31000  : 'south-america',   \n",
    "                    46535  : 'baltic-states',   \n",
    "                    60095  : 'central-africa',   \n",
    "                    55200  : 'eurasia',   \n",
    "                    60070  : 'east-africa',   \n",
    "                    45000  : 'central-eastern-europe',   \n",
    "                    45400  : 'central-eastern-europe',   \n",
    "                    40100  : 'northern-europe',   \n",
    "                    54500  : 'middle-east',   \n",
    "                    60065  : 'east-africa',   \n",
    "                    60036  : 'west-africa',   \n",
    "                    42600  : 'western-europe',   \n",
    "                    10000  : 'us-outlying-areas',   \n",
    "                    60039  : 'west-africa',  \n",
    "                    45730  : 'central-eastern-europe',  \n",
    "                    40000  : 'northern-europe',   \n",
    "                    43610  : 'southern-europe',   \n",
    "                    46300  : 'baltic-states',   \n",
    "                    26070  : 'caribbean',   \n",
    "                    45750  : 'central-eastern-europe',   \n",
    "                    26091  : 'caribbean',   \n",
    "                    26075  : 'caribbean',   \n",
    "                    55300  : 'eurasia',   \n",
    "                    40400  : 'northern-europe',   \n",
    "                    26043  : 'caribbean',   \n",
    "                    60060  : 'east-africa', \n",
    "                    60038  : 'west-africa',   \n",
    "                    60035  : 'west-africa',   \n",
    "                    16010  : 'other-north-america',   \n",
    "                    70020  : 'australia-new-zealand',  \n",
    "                    30070  : 'south-america',   \n",
    "                    45740  : 'central-eastern-europe',   \n",
    "                    45200  : 'central-eastern-europe',  \n",
    "                    26065  : 'caribbean',   \n",
    "                    26055  : 'caribbean',   \n",
    "                    60097  : 'central-africa',   \n",
    "                    54350  : 'middle-east',   \n",
    "                    45212  : 'central-eastern-europe',   \n",
    "                    45213  : 'central-eastern-europe',   \n",
    "                    71022  : 'australia-new-zealand',   \n",
    "                    60019  : 'africa' ,  \n",
    "                    10750  : 'us-outlying-areas',   \n",
    "                    46200  : 'baltic-states',   \n",
    "                    50300  : 'east-asia',   \n",
    "                    40200  : 'northern-europe',   \n",
    "                    45770  : 'central-eastern-europe',   \n",
    "                    60037  : 'west-africa'}, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CONSOLIDATE BIRTHPLACE\n",
    "df['BPL'].replace({'central-eastern-europe':'europe', \n",
    "                    'baltic-states':'europe',  \n",
    "                    'southern-europe':'europe', \n",
    "                    'western-europe':'europe',\n",
    "                    'northern-europe':'europe', \n",
    "                    'central-america':'north-america', \n",
    "                    'caribbean':'north-america',\n",
    "                    'us-outlying-areas':'north-america', \n",
    "                    'other-north-america':'north-america', \n",
    "                    'west-africa':'africa',\n",
    "                    'east-africa':'africa', \n",
    "                    'central-africa':'africa',\n",
    "                    'southern-europe':'europe',\n",
    "                    'western-europe':'europe',\n",
    "                    'northern-europe':'europe',\n",
    "                    'uk-ireland':'europe',\n",
    "                    'eurasia':'asia',\n",
    "                    'middle-east':'asia',\n",
    "                    'southeast-asia':'asia',\n",
    "                    'east-asia':'asia',\n",
    "                    'india-southwest-asia':'asia',\n",
    "                    'central-asia':'asia'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CHANGE YEAR IMMIGRATED TO BOOLEAN\n",
    "df['YRIMMIG'] = np.where(df['YRIMMIG'] > 1948, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#OCCUPATION RECODE\n",
    "conditions = [\n",
    "    df['OCC2010'] == 10,\n",
    "    df['OCC2010'] == 20,\n",
    "    df['OCC2010'] == 30,\n",
    "    df['OCC2010'] == 100,\n",
    "    df['OCC2010'] == 110,\n",
    "    df['OCC2010'] == 120,\n",
    "    df['OCC2010'] == 130,\n",
    "    df['OCC2010'] == 140,\n",
    "    df['OCC2010'] == 150,\n",
    "    df['OCC2010'] == 160,\n",
    "    df['OCC2010'] == 205,\n",
    "    df['OCC2010'] == 220,\n",
    "    df['OCC2010'] == 230,\n",
    "    df['OCC2010'] == 300,\n",
    "    df['OCC2010'] == 310,\n",
    "    df['OCC2010'] == 320,\n",
    "    df['OCC2010'] == 330,\n",
    "    df['OCC2010'] == 350,\n",
    "    df['OCC2010'] == 360,\n",
    "    df['OCC2010'] == 410,\n",
    "    df['OCC2010'] == 420,\n",
    "    df['OCC2010'] == 430,\n",
    "    df['OCC2010'] == 500,\n",
    "    df['OCC2010'] == 510,\n",
    "    df['OCC2010'] == 520,\n",
    "    df['OCC2010'] == 530,\n",
    "    df['OCC2010'] == 540,\n",
    "    df['OCC2010'] == 560,\n",
    "    df['OCC2010'] == 600,\n",
    "    df['OCC2010'] == 620,\n",
    "    df['OCC2010'] == 700,\n",
    "    df['OCC2010'] == 710,\n",
    "    df['OCC2010'] == 720,\n",
    "    df['OCC2010'] == 730,\n",
    "    df['OCC2010'] == 800,\n",
    "    df['OCC2010'] == 810,\n",
    "    df['OCC2010'] == 820,\n",
    "    df['OCC2010'] == 830,\n",
    "    df['OCC2010'] == 840,\n",
    "    df['OCC2010'] == 850,\n",
    "    df['OCC2010'] == 860,\n",
    "    df['OCC2010'] == 900,\n",
    "    df['OCC2010'] == 910,\n",
    "    df['OCC2010'] == 930,\n",
    "    df['OCC2010'] == 940,\n",
    "    df['OCC2010'] == 950,\n",
    "    df['OCC2010'] == 1000,\n",
    "    df['OCC2010'] == 1010,\n",
    "    df['OCC2010'] == 1020,\n",
    "    df['OCC2010'] == 1050,\n",
    "    df['OCC2010'] == 1060,\n",
    "    df['OCC2010'] == 1100,\n",
    "    df['OCC2010'] == 1200,\n",
    "    df['OCC2010'] == 1220,\n",
    "    df['OCC2010'] == 1230,\n",
    "    df['OCC2010'] == 1240,\n",
    "    df['OCC2010'] == 1300,\n",
    "    df['OCC2010'] == 1310,\n",
    "    df['OCC2010'] == 1320,\n",
    "    df['OCC2010'] == 1350,\n",
    "    df['OCC2010'] == 1360,\n",
    "    df['OCC2010'] == 1400,\n",
    "    df['OCC2010'] == 1410,\n",
    "    df['OCC2010'] == 1420,\n",
    "    df['OCC2010'] == 1430,\n",
    "    df['OCC2010'] == 1440,\n",
    "    df['OCC2010'] == 1450,\n",
    "    df['OCC2010'] == 1460,\n",
    "    df['OCC2010'] == 1520,\n",
    "    df['OCC2010'] == 1530,\n",
    "    df['OCC2010'] == 1540,\n",
    "    df['OCC2010'] == 1550,\n",
    "    df['OCC2010'] == 1560,\n",
    "    df['OCC2010'] == 1600,\n",
    "    df['OCC2010'] == 1610,\n",
    "    df['OCC2010'] == 1640,\n",
    "    df['OCC2010'] == 1650,\n",
    "    df['OCC2010'] == 1700,\n",
    "    df['OCC2010'] == 1710,\n",
    "    df['OCC2010'] == 1720,\n",
    "    df['OCC2010'] == 1740,\n",
    "    df['OCC2010'] == 1760,\n",
    "    df['OCC2010'] == 1800,\n",
    "    df['OCC2010'] == 1820,\n",
    "    df['OCC2010'] == 1830,\n",
    "    df['OCC2010'] == 1840,\n",
    "    df['OCC2010'] == 1900,\n",
    "    df['OCC2010'] == 1910,\n",
    "    df['OCC2010'] == 1920,\n",
    "    df['OCC2010'] == 1930,\n",
    "    df['OCC2010'] == 1960,\n",
    "    df['OCC2010'] == 1980,\n",
    "    df['OCC2010'] == 2000,\n",
    "    df['OCC2010'] == 2010,\n",
    "    df['OCC2010'] == 2020,\n",
    "    df['OCC2010'] == 2040,\n",
    "    df['OCC2010'] == 2050,\n",
    "    df['OCC2010'] == 2060,\n",
    "    df['OCC2010'] == 2100,\n",
    "    df['OCC2010'] == 2140,\n",
    "    df['OCC2010'] == 2150,\n",
    "    df['OCC2010'] == 2200,\n",
    "    df['OCC2010'] == 2300,\n",
    "    df['OCC2010'] == 2310,\n",
    "    df['OCC2010'] == 2320,\n",
    "    df['OCC2010'] == 2330,\n",
    "    df['OCC2010'] == 2340,\n",
    "    df['OCC2010'] == 2400,\n",
    "    df['OCC2010'] == 2430,\n",
    "    df['OCC2010'] == 2440,\n",
    "    df['OCC2010'] == 2540,\n",
    "    df['OCC2010'] == 2550,\n",
    "    df['OCC2010'] == 2600,\n",
    "    df['OCC2010'] == 2630,\n",
    "    df['OCC2010'] == 2700,\n",
    "    df['OCC2010'] == 2720,\n",
    "    df['OCC2010'] == 2740,\n",
    "    df['OCC2010'] == 2750,\n",
    "    df['OCC2010'] == 2760,\n",
    "    df['OCC2010'] == 2800,\n",
    "    df['OCC2010'] == 2810,\n",
    "    df['OCC2010'] == 2825,\n",
    "    df['OCC2010'] == 2840,\n",
    "    df['OCC2010'] == 2850,\n",
    "    df['OCC2010'] == 2860,\n",
    "    df['OCC2010'] == 2900,\n",
    "    df['OCC2010'] == 2910,\n",
    "    df['OCC2010'] == 2920,\n",
    "    df['OCC2010'] == 3000,\n",
    "    df['OCC2010'] == 3010,\n",
    "    df['OCC2010'] == 3030,\n",
    "    df['OCC2010'] == 3040,\n",
    "    df['OCC2010'] == 3050,\n",
    "    df['OCC2010'] == 3060,\n",
    "    df['OCC2010'] == 3110,\n",
    "    df['OCC2010'] == 3120,\n",
    "    df['OCC2010'] == 3130,\n",
    "    df['OCC2010'] == 3140,\n",
    "    df['OCC2010'] == 3150,\n",
    "    df['OCC2010'] == 3160,\n",
    "    df['OCC2010'] == 3200,\n",
    "    df['OCC2010'] == 3210,\n",
    "    df['OCC2010'] == 3220,\n",
    "    df['OCC2010'] == 3230,\n",
    "    df['OCC2010'] == 3240,\n",
    "    df['OCC2010'] == 3250,\n",
    "    df['OCC2010'] == 3260,\n",
    "    df['OCC2010'] == 3300,\n",
    "    df['OCC2010'] == 3310,\n",
    "    df['OCC2010'] == 3320,\n",
    "    df['OCC2010'] == 3400,\n",
    "    df['OCC2010'] == 3410,\n",
    "    df['OCC2010'] == 3500,\n",
    "    df['OCC2010'] == 3510,\n",
    "    df['OCC2010'] == 3520,\n",
    "    df['OCC2010'] == 3530,\n",
    "    df['OCC2010'] == 3540,\n",
    "    df['OCC2010'] == 3600,\n",
    "    df['OCC2010'] == 3610,\n",
    "    df['OCC2010'] == 3620,\n",
    "    df['OCC2010'] == 3630,\n",
    "    df['OCC2010'] == 3640,\n",
    "    df['OCC2010'] == 3650,\n",
    "    df['OCC2010'] == 3700,\n",
    "    df['OCC2010'] == 3710,\n",
    "    df['OCC2010'] == 3720,\n",
    "    df['OCC2010'] == 3730,\n",
    "    df['OCC2010'] == 3740,\n",
    "    df['OCC2010'] == 3750,\n",
    "    df['OCC2010'] == 3800,\n",
    "    df['OCC2010'] == 3820,\n",
    "    df['OCC2010'] == 3900,\n",
    "    df['OCC2010'] == 3910,\n",
    "    df['OCC2010'] == 3930,\n",
    "    df['OCC2010'] == 3940,\n",
    "    df['OCC2010'] == 3950,\n",
    "    df['OCC2010'] == 4000,\n",
    "    df['OCC2010'] == 4010,\n",
    "    df['OCC2010'] == 4030,\n",
    "    df['OCC2010'] == 4040,\n",
    "    df['OCC2010'] == 4050,\n",
    "    df['OCC2010'] == 4060,\n",
    "    df['OCC2010'] == 4110,\n",
    "    df['OCC2010'] == 4120,\n",
    "    df['OCC2010'] == 4130,\n",
    "    df['OCC2010'] == 4140,\n",
    "    df['OCC2010'] == 4150,\n",
    "    df['OCC2010'] == 4200,\n",
    "    df['OCC2010'] == 4210,\n",
    "    df['OCC2010'] == 4220,\n",
    "    df['OCC2010'] == 4230,\n",
    "    df['OCC2010'] == 4240,\n",
    "    df['OCC2010'] == 4250,\n",
    "    df['OCC2010'] == 4300,\n",
    "    df['OCC2010'] == 4320,\n",
    "    df['OCC2010'] == 4340,\n",
    "    df['OCC2010'] == 4350,\n",
    "    df['OCC2010'] == 4400,\n",
    "    df['OCC2010'] == 4420,\n",
    "    df['OCC2010'] == 4430,\n",
    "    df['OCC2010'] == 4460,\n",
    "    df['OCC2010'] == 4500,\n",
    "    df['OCC2010'] == 4510,\n",
    "    df['OCC2010'] == 4520,\n",
    "    df['OCC2010'] == 4530,\n",
    "    df['OCC2010'] == 4540,\n",
    "    df['OCC2010'] == 4600,\n",
    "    df['OCC2010'] == 4610,\n",
    "    df['OCC2010'] == 4620,\n",
    "    df['OCC2010'] == 4640,\n",
    "    df['OCC2010'] == 4650,\n",
    "    df['OCC2010'] == 4700,\n",
    "    df['OCC2010'] == 4720,\n",
    "    df['OCC2010'] == 4740,\n",
    "    df['OCC2010'] == 4750,\n",
    "    df['OCC2010'] == 4760,\n",
    "    df['OCC2010'] == 4800,\n",
    "    df['OCC2010'] == 4810,\n",
    "    df['OCC2010'] == 4820,\n",
    "    df['OCC2010'] == 4830,\n",
    "    df['OCC2010'] == 4840,\n",
    "    df['OCC2010'] == 4850,\n",
    "    df['OCC2010'] == 4900,\n",
    "    df['OCC2010'] == 4920,\n",
    "    df['OCC2010'] == 4930,\n",
    "    df['OCC2010'] == 4940,\n",
    "    df['OCC2010'] == 4950,\n",
    "    df['OCC2010'] == 4965,\n",
    "    df['OCC2010'] == 5000,\n",
    "    df['OCC2010'] == 5010,\n",
    "    df['OCC2010'] == 5020,\n",
    "    df['OCC2010'] == 5030,\n",
    "    df['OCC2010'] == 5100,\n",
    "    df['OCC2010'] == 5110,\n",
    "    df['OCC2010'] == 5120,\n",
    "    df['OCC2010'] == 5130,\n",
    "    df['OCC2010'] == 5140,\n",
    "    df['OCC2010'] == 5150,\n",
    "    df['OCC2010'] == 5160,\n",
    "    df['OCC2010'] == 5165,\n",
    "    df['OCC2010'] == 5200,\n",
    "    df['OCC2010'] == 5220,\n",
    "    df['OCC2010'] == 5230,\n",
    "    df['OCC2010'] == 5240,\n",
    "    df['OCC2010'] == 5250,\n",
    "    df['OCC2010'] == 5260,\n",
    "    df['OCC2010'] == 5300,\n",
    "    df['OCC2010'] == 5310,\n",
    "    df['OCC2010'] == 5320,\n",
    "    df['OCC2010'] == 5330,\n",
    "    df['OCC2010'] == 5340,\n",
    "    df['OCC2010'] == 5350,\n",
    "    df['OCC2010'] == 5360,\n",
    "    df['OCC2010'] == 5400,\n",
    "    df['OCC2010'] == 5410,\n",
    "    df['OCC2010'] == 5420,\n",
    "    df['OCC2010'] == 5500,\n",
    "    df['OCC2010'] == 5510,\n",
    "    df['OCC2010'] == 5520,\n",
    "    df['OCC2010'] == 5530,\n",
    "    df['OCC2010'] == 5540,\n",
    "    df['OCC2010'] == 5550,\n",
    "    df['OCC2010'] == 5560,\n",
    "    df['OCC2010'] == 5600,\n",
    "    df['OCC2010'] == 5610,\n",
    "    df['OCC2010'] == 5620,\n",
    "    df['OCC2010'] == 5630,\n",
    "    df['OCC2010'] == 5700,\n",
    "    df['OCC2010'] == 5800,\n",
    "    df['OCC2010'] == 5810,\n",
    "    df['OCC2010'] == 5820,\n",
    "    df['OCC2010'] == 5840,\n",
    "    df['OCC2010'] == 5850,\n",
    "    df['OCC2010'] == 5860,\n",
    "    df['OCC2010'] == 5900,\n",
    "    df['OCC2010'] == 5910,\n",
    "    df['OCC2010'] == 5920,\n",
    "    df['OCC2010'] == 5940,\n",
    "    df['OCC2010'] == 6005,\n",
    "    df['OCC2010'] == 6010,\n",
    "    df['OCC2010'] == 6040,\n",
    "    df['OCC2010'] == 6050,\n",
    "    df['OCC2010'] == 6100,\n",
    "    df['OCC2010'] == 6120,\n",
    "    df['OCC2010'] == 6130,\n",
    "    df['OCC2010'] == 6200,\n",
    "    df['OCC2010'] == 6210,\n",
    "    df['OCC2010'] == 6220,\n",
    "    df['OCC2010'] == 6230,\n",
    "    df['OCC2010'] == 6240,\n",
    "    df['OCC2010'] == 6250,\n",
    "    df['OCC2010'] == 6260,\n",
    "    df['OCC2010'] == 6300,\n",
    "    df['OCC2010'] == 6320,\n",
    "    df['OCC2010'] == 6330,\n",
    "    df['OCC2010'] == 6355,\n",
    "    df['OCC2010'] == 6360,\n",
    "    df['OCC2010'] == 6400,\n",
    "    df['OCC2010'] == 6420,\n",
    "    df['OCC2010'] == 6430,\n",
    "    df['OCC2010'] == 6440,\n",
    "    df['OCC2010'] == 6460,\n",
    "    df['OCC2010'] == 6500,\n",
    "    df['OCC2010'] == 6515,\n",
    "    df['OCC2010'] == 6520,\n",
    "    df['OCC2010'] == 6530,\n",
    "    df['OCC2010'] == 6600,\n",
    "    df['OCC2010'] == 6660,\n",
    "    df['OCC2010'] == 6700,\n",
    "    df['OCC2010'] == 6710,\n",
    "    df['OCC2010'] == 6720,\n",
    "    df['OCC2010'] == 6730,\n",
    "    df['OCC2010'] == 6740,\n",
    "    df['OCC2010'] == 6765,\n",
    "    df['OCC2010'] == 6800,\n",
    "    df['OCC2010'] == 6820,\n",
    "    df['OCC2010'] == 6830,\n",
    "    df['OCC2010'] == 6840,\n",
    "    df['OCC2010'] == 6940,\n",
    "    df['OCC2010'] == 7000,\n",
    "    df['OCC2010'] == 7010,\n",
    "    df['OCC2010'] == 7020,\n",
    "    df['OCC2010'] == 7030,\n",
    "    df['OCC2010'] == 7040,\n",
    "    df['OCC2010'] == 7100,\n",
    "    df['OCC2010'] == 7110,\n",
    "    df['OCC2010'] == 7120,\n",
    "    df['OCC2010'] == 7125,\n",
    "    df['OCC2010'] == 7130,\n",
    "    df['OCC2010'] == 7140,\n",
    "    df['OCC2010'] == 7150,\n",
    "    df['OCC2010'] == 7160,\n",
    "    df['OCC2010'] == 7200,\n",
    "    df['OCC2010'] == 7210,\n",
    "    df['OCC2010'] == 7220,\n",
    "    df['OCC2010'] == 7240,\n",
    "    df['OCC2010'] == 7260,\n",
    "    df['OCC2010'] == 7300,\n",
    "    df['OCC2010'] == 7315,\n",
    "    df['OCC2010'] == 7320,\n",
    "    df['OCC2010'] == 7330,\n",
    "    df['OCC2010'] == 7340,\n",
    "    df['OCC2010'] == 7350,\n",
    "    df['OCC2010'] == 7360,\n",
    "    df['OCC2010'] == 7410,\n",
    "    df['OCC2010'] == 7420,\n",
    "    df['OCC2010'] == 7430,\n",
    "    df['OCC2010'] == 7510,\n",
    "    df['OCC2010'] == 7540,\n",
    "    df['OCC2010'] == 7550,\n",
    "    df['OCC2010'] == 7560,\n",
    "    df['OCC2010'] == 7610,\n",
    "    df['OCC2010'] == 7630,\n",
    "    df['OCC2010'] == 7700,\n",
    "    df['OCC2010'] == 7710,\n",
    "    df['OCC2010'] == 7720,\n",
    "    df['OCC2010'] == 7730,\n",
    "    df['OCC2010'] == 7740,\n",
    "    df['OCC2010'] == 7750,\n",
    "    df['OCC2010'] == 7800,\n",
    "    df['OCC2010'] == 7810,\n",
    "    df['OCC2010'] == 7830,\n",
    "    df['OCC2010'] == 7840,\n",
    "    df['OCC2010'] == 7850,\n",
    "    df['OCC2010'] == 7855,\n",
    "    df['OCC2010'] == 7900,\n",
    "    df['OCC2010'] == 7920,\n",
    "    df['OCC2010'] == 7930,\n",
    "    df['OCC2010'] == 7940,\n",
    "    df['OCC2010'] == 7950,\n",
    "    df['OCC2010'] == 7960,\n",
    "    df['OCC2010'] == 8000,\n",
    "    df['OCC2010'] == 8010,\n",
    "    df['OCC2010'] == 8030,\n",
    "    df['OCC2010'] == 8040,\n",
    "    df['OCC2010'] == 8060,\n",
    "    df['OCC2010'] == 8100,\n",
    "    df['OCC2010'] == 8130,\n",
    "    df['OCC2010'] == 8140,\n",
    "    df['OCC2010'] == 8150,\n",
    "    df['OCC2010'] == 8200,\n",
    "    df['OCC2010'] == 8210,\n",
    "    df['OCC2010'] == 8220,\n",
    "    df['OCC2010'] == 8230,\n",
    "    df['OCC2010'] == 8250,\n",
    "    df['OCC2010'] == 8300,\n",
    "    df['OCC2010'] == 8310,\n",
    "    df['OCC2010'] == 8320,\n",
    "    df['OCC2010'] == 8330,\n",
    "    df['OCC2010'] == 8340,\n",
    "    df['OCC2010'] == 8350,\n",
    "    df['OCC2010'] == 8400,\n",
    "    df['OCC2010'] == 8410,\n",
    "    df['OCC2010'] == 8420,\n",
    "    df['OCC2010'] == 8450,\n",
    "    df['OCC2010'] == 8460,\n",
    "    df['OCC2010'] == 8500,\n",
    "    df['OCC2010'] == 8510,\n",
    "    df['OCC2010'] == 8530,\n",
    "    df['OCC2010'] == 8540,\n",
    "    df['OCC2010'] == 8550,\n",
    "    df['OCC2010'] == 8600,\n",
    "    df['OCC2010'] == 8610,\n",
    "    df['OCC2010'] == 8620,\n",
    "    df['OCC2010'] == 8630,\n",
    "    df['OCC2010'] == 8640,\n",
    "    df['OCC2010'] == 8650,\n",
    "    df['OCC2010'] == 8710,\n",
    "    df['OCC2010'] == 8720,\n",
    "    df['OCC2010'] == 8730,\n",
    "    df['OCC2010'] == 8740,\n",
    "    df['OCC2010'] == 8750,\n",
    "    df['OCC2010'] == 8760,\n",
    "    df['OCC2010'] == 8800,\n",
    "    df['OCC2010'] == 8810,\n",
    "    df['OCC2010'] == 8830,\n",
    "    df['OCC2010'] == 8850,\n",
    "    df['OCC2010'] == 8860,\n",
    "    df['OCC2010'] == 8910,\n",
    "    df['OCC2010'] == 8920,\n",
    "    df['OCC2010'] == 8930,\n",
    "    df['OCC2010'] == 8940,\n",
    "    df['OCC2010'] == 8950,\n",
    "    df['OCC2010'] == 8965,\n",
    "    df['OCC2010'] == 9000,\n",
    "    df['OCC2010'] == 9030,\n",
    "    df['OCC2010'] == 9040,\n",
    "    df['OCC2010'] == 9050,\n",
    "    df['OCC2010'] == 9100,\n",
    "    df['OCC2010'] == 9130,\n",
    "    df['OCC2010'] == 9140,\n",
    "    df['OCC2010'] == 9150,\n",
    "    df['OCC2010'] == 9200,\n",
    "    df['OCC2010'] == 9230,\n",
    "    df['OCC2010'] == 9240,\n",
    "    df['OCC2010'] == 9260,\n",
    "    df['OCC2010'] == 9300,\n",
    "    df['OCC2010'] == 9310,\n",
    "    df['OCC2010'] == 9350,\n",
    "    df['OCC2010'] == 9360,\n",
    "    df['OCC2010'] == 9410,\n",
    "    df['OCC2010'] == 9420,\n",
    "    df['OCC2010'] == 9510,\n",
    "    df['OCC2010'] == 9520,\n",
    "    df['OCC2010'] == 9560,\n",
    "    df['OCC2010'] == 9600,\n",
    "    df['OCC2010'] == 9610,\n",
    "    df['OCC2010'] == 9620,\n",
    "    df['OCC2010'] == 9630,\n",
    "    df['OCC2010'] == 9640,\n",
    "    df['OCC2010'] == 9650,\n",
    "    df['OCC2010'] == 9720,\n",
    "    df['OCC2010'] == 9750,\n",
    "    df['OCC2010'] == 9800,\n",
    "    df['OCC2010'] == 9810,\n",
    "    df['OCC2010'] == 9820,\n",
    "    df['OCC2010'] == 9830,\n",
    "    df['OCC2010'] == 9920,\n",
    "    df['OCC2010'] == 9999\n",
    "        \n",
    "]\n",
    "\n",
    "choices = [\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'MANAGEMENT, BUSINESS, SCIENCE, AND ARTS',\n",
    "'BUSINESS OPERATIONS SPECIALISTS',\n",
    "'BUSINESS OPERATIONS SPECIALISTS',\n",
    "'BUSINESS OPERATIONS SPECIALISTS',\n",
    "'BUSINESS OPERATIONS SPECIALISTS',\n",
    "'BUSINESS OPERATIONS SPECIALISTS',\n",
    "'BUSINESS OPERATIONS SPECIALISTS',\n",
    "'BUSINESS OPERATIONS SPECIALISTS',\n",
    "'BUSINESS OPERATIONS SPECIALISTS',\n",
    "'BUSINESS OPERATIONS SPECIALISTS',\n",
    "'BUSINESS OPERATIONS SPECIALISTS',\n",
    "'BUSINESS OPERATIONS SPECIALISTS',\n",
    "'BUSINESS OPERATIONS SPECIALISTS',\n",
    "'FINANCIAL SPECIALISTS',\n",
    "'FINANCIAL SPECIALISTS',\n",
    "'FINANCIAL SPECIALISTS',\n",
    "'FINANCIAL SPECIALISTS',\n",
    "'FINANCIAL SPECIALISTS',\n",
    "'FINANCIAL SPECIALISTS',\n",
    "'FINANCIAL SPECIALISTS',\n",
    "'FINANCIAL SPECIALISTS',\n",
    "'FINANCIAL SPECIALISTS',\n",
    "'FINANCIAL SPECIALISTS',\n",
    "'FINANCIAL SPECIALISTS',\n",
    "'FINANCIAL SPECIALISTS',\n",
    "'COMPUTER AND MATHEMATICAL',\n",
    "'COMPUTER AND MATHEMATICAL',\n",
    "'COMPUTER AND MATHEMATICAL',\n",
    "'COMPUTER AND MATHEMATICAL',\n",
    "'COMPUTER AND MATHEMATICAL',\n",
    "'COMPUTER AND MATHEMATICAL',\n",
    "'COMPUTER AND MATHEMATICAL',\n",
    "'COMPUTER AND MATHEMATICAL',\n",
    "'COMPUTER AND MATHEMATICAL',\n",
    "'COMPUTER AND MATHEMATICAL',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'ARCHITECTURE AND ENGINEERING',\n",
    "'TECHNICIANS',\n",
    "'TECHNICIANS',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'LIFE, PHYSICAL, AND SOCIAL SCIENCE',\n",
    "'COMMUNITY AND SOCIAL SERVICES2000',\n",
    "'COMMUNITY AND SOCIAL SERVICES2010',\n",
    "'COMMUNITY AND SOCIAL SERVICES2020',\n",
    "'COMMUNITY AND SOCIAL SERVICES2040',\n",
    "'COMMUNITY AND SOCIAL SERVICES2050',\n",
    "'COMMUNITY AND SOCIAL SERVICES2050',\n",
    "'LEGAL',\n",
    "'LEGAL',\n",
    "'LEGAL',\n",
    "'EDUCATION, TRAINING, AND LIBRARY',\n",
    "'EDUCATION, TRAINING, AND LIBRARY',\n",
    "'EDUCATION, TRAINING, AND LIBRARY',\n",
    "'EDUCATION, TRAINING, AND LIBRARY',\n",
    "'EDUCATION, TRAINING, AND LIBRARY',\n",
    "'EDUCATION, TRAINING, AND LIBRARY',\n",
    "'EDUCATION, TRAINING, AND LIBRARY',\n",
    "'EDUCATION, TRAINING, AND LIBRARY',\n",
    "'EDUCATION, TRAINING, AND LIBRARY',\n",
    "'EDUCATION, TRAINING, AND LIBRARY',\n",
    "'EDUCATION, TRAINING, AND LIBRARY',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'ARTS, DESIGN, ENTERTAINMENT, SPORTS, AND MEDIA',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE PRACTITIONERS AND TECHNICAL',\n",
    "'HEALTHCARE SUPPORT',\n",
    "'HEALTHCARE SUPPORT',\n",
    "'HEALTHCARE SUPPORT',\n",
    "'HEALTHCARE SUPPORT',\n",
    "'HEALTHCARE SUPPORT',\n",
    "'HEALTHCARE SUPPORT',\n",
    "'PROTECTIVE SERVICE',\n",
    "'PROTECTIVE SERVICE',\n",
    "'PROTECTIVE SERVICE',\n",
    "'PROTECTIVE SERVICE',\n",
    "'PROTECTIVE SERVICE',\n",
    "'PROTECTIVE SERVICE',\n",
    "'PROTECTIVE SERVICE',\n",
    "'PROTECTIVE SERVICE',\n",
    "'PROTECTIVE SERVICE',\n",
    "'PROTECTIVE SERVICE',\n",
    "'PROTECTIVE SERVICE',\n",
    "'PROTECTIVE SERVICE',\n",
    "'PROTECTIVE SERVICE',\n",
    "'FOOD PREPARATION AND SERVING',\n",
    "'FOOD PREPARATION AND SERVING',\n",
    "'FOOD PREPARATION AND SERVING',\n",
    "'FOOD PREPARATION AND SERVING',\n",
    "'FOOD PREPARATION AND SERVING',\n",
    "'FOOD PREPARATION AND SERVING',\n",
    "'FOOD PREPARATION AND SERVING',\n",
    "'FOOD PREPARATION AND SERVING',\n",
    "'FOOD PREPARATION AND SERVING',\n",
    "'FOOD PREPARATION AND SERVING',\n",
    "'FOOD PREPARATION AND SERVING',\n",
    "'BUILDING AND GROUNDS CLEANING AND MAINTENANCE',\n",
    "'BUILDING AND GROUNDS CLEANING AND MAINTENANCE',\n",
    "'BUILDING AND GROUNDS CLEANING AND MAINTENANCE',\n",
    "'BUILDING AND GROUNDS CLEANING AND MAINTENANCE',\n",
    "'BUILDING AND GROUNDS CLEANING AND MAINTENANCE',\n",
    "'BUILDING AND GROUNDS CLEANING AND MAINTENANCE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'PERSONAL CARE AND SERVICE',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'SALES AND RELATED',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'OFFICE AND ADMINISTRATIVE SUPPORT',\n",
    "'FARMING, FISHING, AND FORESTRY',\n",
    "'FARMING, FISHING, AND FORESTRY',\n",
    "'FARMING, FISHING, AND FORESTRY',\n",
    "'FARMING, FISHING, AND FORESTRY',\n",
    "'FARMING, FISHING, AND FORESTRY',\n",
    "'FARMING, FISHING, AND FORESTRY',\n",
    "'FARMING, FISHING, AND FORESTRY',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'CONSTRUCTION',\n",
    "'EXTRACTION',\n",
    "'EXTRACTION',\n",
    "'EXTRACTION',\n",
    "'EXTRACTION',\n",
    "'EXTRACTION',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'INSTALLATION, MAINTENANCE, AND REPAIR',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'PRODUCTION',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'TRANSPORTATION AND MATERIAL MOVING',\n",
    "'MILITARY SPECIFIC',\n",
    "'MILITARY SPECIFIC',\n",
    "'MILITARY SPECIFIC',\n",
    "'MILITARY SPECIFIC',\n",
    "'UNEMPLOYED OR NEVER WORKED',\n",
    "'UNEMPLOYED OR NEVER WORKED'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['OCC2010'] = np.select(conditions, choices, df['OCC2010'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#EDUCATION RECODE\n",
    "conditions = [\n",
    "    df['EDUC'] == 0,\n",
    "    df['EDUC'] == 1,\n",
    "    df['EDUC'] == 2,\n",
    "    df['EDUC'] == 10,\n",
    "    df['EDUC'] == 11,\n",
    "    df['EDUC'] == 12,\n",
    "    df['EDUC'] == 13,\n",
    "    df['EDUC'] == 14,\n",
    "    df['EDUC'] == 20,\n",
    "    df['EDUC'] == 21,\n",
    "    df['EDUC'] == 22,\n",
    "    df['EDUC'] == 30,\n",
    "    df['EDUC'] == 31,\n",
    "    df['EDUC'] == 32,\n",
    "    df['EDUC'] == 40,\n",
    "    df['EDUC'] == 50,\n",
    "    df['EDUC'] == 60,\n",
    "    df['EDUC'] == 70,\n",
    "    df['EDUC'] == 71,\n",
    "    df['EDUC'] == 72,\n",
    "    df['EDUC'] == 73,\n",
    "    df['EDUC'] == 80,\n",
    "    df['EDUC'] == 81,\n",
    "    df['EDUC'] == 90,\n",
    "    df['EDUC'] == 91,\n",
    "    df['EDUC'] == 92,\n",
    "    df['EDUC'] == 100,\n",
    "    df['EDUC'] == 110,\n",
    "    df['EDUC'] == 111,\n",
    "    df['EDUC'] == 120,\n",
    "    df['EDUC'] == 121,\n",
    "    df['EDUC'] == 122,\n",
    "    df['EDUC'] == 123,\n",
    "    df['EDUC'] == 124,\n",
    "    df['EDUC'] == 125,\n",
    "    df['EDUC'] == 999\n",
    "    \n",
    "]\n",
    "\n",
    "choices = [\n",
    "     'NIU or no schooling'\n",
    "    ,'NIU or blank'\n",
    "    ,'None or preschool'\n",
    "    ,'Grades 1, 2, 3, or 4'\n",
    "    ,'Grade 1'\n",
    "    ,'Grade 2'\n",
    "    ,'Grade 3'\n",
    "    ,'Grade 4'\n",
    "    ,'Grades 5 or 6'\n",
    "    ,'Grade 5'\n",
    "    ,'Grade 6'\n",
    "    ,'Grades 7 or 8'\n",
    "    ,'Grade 7'\n",
    "    ,'Grade 8'\n",
    "    ,'Grade 9'\n",
    "    ,'Grade 10'\n",
    "    ,'Grade 11'\n",
    "    ,'Grade 12'\n",
    "    ,'12th grade, no diploma'\n",
    "    ,'12th grade, diploma unclear'\n",
    "    ,'High school diploma or equivalent'\n",
    "    ,'1 year of college'\n",
    "    ,'Some college but no degree'\n",
    "    ,'2 years of college'\n",
    "    ,\"Associate's degree, occupational/vocational program\"\n",
    "    ,\"Associate's degree, academic program\"\n",
    "    ,'3 years of college'\n",
    "    ,'4 years of college'\n",
    "    ,\"Bachelor's degree\"\n",
    "    ,'5+ years of college'\n",
    "    ,'5 years of college'\n",
    "    ,'6+ years of college'\n",
    "    ,\"Master's degree\"\n",
    "    ,'Professional school degree'\n",
    "    ,'Doctorate degree'\n",
    "    ,'Missing/Unknown'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['EDUC'] = np.select(conditions, choices, df['EDUC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#CLASSWRK RECODE\n",
    "conditions = [\n",
    "    df['CLASSWKR'] == 0,\n",
    "    df['CLASSWKR'] == 10,\n",
    "    df['CLASSWKR'] == 13,\n",
    "    df['CLASSWKR'] == 14,\n",
    "    df['CLASSWKR'] == 20,\n",
    "    df['CLASSWKR'] == 21,\n",
    "    df['CLASSWKR'] == 22,\n",
    "    df['CLASSWKR'] == 23,\n",
    "    df['CLASSWKR'] == 24,\n",
    "    df['CLASSWKR'] == 25,\n",
    "    df['CLASSWKR'] == 26,\n",
    "    df['CLASSWKR'] == 27,\n",
    "    df['CLASSWKR'] == 28,\n",
    "    df['CLASSWKR'] == 29,\n",
    "    df['CLASSWKR'] == 99\n",
    "]\n",
    "\n",
    "choices = [\n",
    "    'NIU'\n",
    "    ,'Self-employed'\n",
    "    ,'Self-employed, not incorporated'\n",
    "    ,'Self-employed, incorporated'\n",
    "    ,'Works for wages or salary'\n",
    "    ,'Wage/salary, private'\n",
    "    ,'Private, for profit'\n",
    "    ,'Private, nonprofit'\n",
    "    ,'Wage/salary, government'\n",
    "    ,'Federal government employee'\n",
    "    ,'Armed forces'\n",
    "    ,'State government employee'\n",
    "    ,'Local government employee'\n",
    "    ,'Unpaid family worker'\n",
    "    ,'Missing/Unknown'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['CLASSWKR'] = np.select(conditions, choices, df['CLASSWKR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CONSOLIDATE CLASSWKR\n",
    "conditions = [\n",
    "    df['CLASSWKR'] == 'Wage/salary, private',            \n",
    "    df['CLASSWKR'] == 'Local government employee',          \n",
    "    df['CLASSWKR'] == 'Self-employed, not incorporated',    \n",
    "    df['CLASSWKR'] == 'State government employee',          \n",
    "    df['CLASSWKR'] == 'Self-employed, incorporated',        \n",
    "    df['CLASSWKR'] == 'Federal government employee'\n",
    "]\n",
    "\n",
    "choices = [\n",
    "    'Private',\n",
    "    'Governemnt',\n",
    "    'Self-Employed',\n",
    "    'Governemnt',\n",
    "    'Self-Employed',\n",
    "    'Governemnt'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['CLASSWKR'] = np.select(conditions,choices,df['CLASSWKR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#INDUSTRY RECODE\n",
    "conditions = [\n",
    "    df['IND1990'] == 0,\n",
    "    df['IND1990'] == 10,\n",
    "    df['IND1990'] == 11,\n",
    "    df['IND1990'] == 12,\n",
    "    df['IND1990'] == 20,\n",
    "    df['IND1990'] == 30,\n",
    "    df['IND1990'] == 31,\n",
    "    df['IND1990'] == 32,\n",
    "    df['IND1990'] == 40,\n",
    "    df['IND1990'] == 41,\n",
    "    df['IND1990'] == 42,\n",
    "    df['IND1990'] == 50,\n",
    "    df['IND1990'] == 60,\n",
    "    df['IND1990'] == 100,\n",
    "    df['IND1990'] == 101,\n",
    "    df['IND1990'] == 102,\n",
    "    df['IND1990'] == 110,\n",
    "    df['IND1990'] == 111,\n",
    "    df['IND1990'] == 112,\n",
    "    df['IND1990'] == 120,\n",
    "    df['IND1990'] == 121,\n",
    "    df['IND1990'] == 122,\n",
    "    df['IND1990'] == 130,\n",
    "    df['IND1990'] == 132,\n",
    "    df['IND1990'] == 140,\n",
    "    df['IND1990'] == 141,\n",
    "    df['IND1990'] == 142,\n",
    "    df['IND1990'] == 150,\n",
    "    df['IND1990'] == 151,\n",
    "    df['IND1990'] == 152,\n",
    "    df['IND1990'] == 160,\n",
    "    df['IND1990'] == 161,\n",
    "    df['IND1990'] == 162,\n",
    "    df['IND1990'] == 171,\n",
    "    df['IND1990'] == 172,\n",
    "    df['IND1990'] == 180,\n",
    "    df['IND1990'] == 181,\n",
    "    df['IND1990'] == 182,\n",
    "    df['IND1990'] == 190,\n",
    "    df['IND1990'] == 191,\n",
    "    df['IND1990'] == 192,\n",
    "    df['IND1990'] == 200,\n",
    "    df['IND1990'] == 201,\n",
    "    df['IND1990'] == 210,\n",
    "    df['IND1990'] == 211,\n",
    "    df['IND1990'] == 212,\n",
    "    df['IND1990'] == 220,\n",
    "    df['IND1990'] == 221,\n",
    "    df['IND1990'] == 222,\n",
    "    df['IND1990'] == 229,\n",
    "    df['IND1990'] == 230,\n",
    "    df['IND1990'] == 231,\n",
    "    df['IND1990'] == 232,\n",
    "    df['IND1990'] == 241,\n",
    "    df['IND1990'] == 242,\n",
    "    df['IND1990'] == 250,\n",
    "    df['IND1990'] == 251,\n",
    "    df['IND1990'] == 252,\n",
    "    df['IND1990'] == 261,\n",
    "    df['IND1990'] == 262,\n",
    "    df['IND1990'] == 270,\n",
    "    df['IND1990'] == 271,\n",
    "    df['IND1990'] == 272,\n",
    "    df['IND1990'] == 280,\n",
    "    df['IND1990'] == 281,\n",
    "    df['IND1990'] == 282,\n",
    "    df['IND1990'] == 290,\n",
    "    df['IND1990'] == 291,\n",
    "    df['IND1990'] == 292,\n",
    "    df['IND1990'] == 300,\n",
    "    df['IND1990'] == 301,\n",
    "    df['IND1990'] == 310,\n",
    "    df['IND1990'] == 311,\n",
    "    df['IND1990'] == 312,\n",
    "    df['IND1990'] == 320,\n",
    "    df['IND1990'] == 321,\n",
    "    df['IND1990'] == 322,\n",
    "    df['IND1990'] == 331,\n",
    "    df['IND1990'] == 332,\n",
    "    df['IND1990'] == 340,\n",
    "    df['IND1990'] == 341,\n",
    "    df['IND1990'] == 342,\n",
    "    df['IND1990'] == 350,\n",
    "    df['IND1990'] == 351,\n",
    "    df['IND1990'] == 352,\n",
    "    df['IND1990'] == 360,\n",
    "    df['IND1990'] == 361,\n",
    "    df['IND1990'] == 362,\n",
    "    df['IND1990'] == 370,\n",
    "    df['IND1990'] == 371,\n",
    "    df['IND1990'] == 372,\n",
    "    df['IND1990'] == 380,\n",
    "    df['IND1990'] == 381,\n",
    "    df['IND1990'] == 390,\n",
    "    df['IND1990'] == 391,\n",
    "    df['IND1990'] == 392,\n",
    "    df['IND1990'] == 400,\n",
    "    df['IND1990'] == 401,\n",
    "    df['IND1990'] == 402,\n",
    "    df['IND1990'] == 410,\n",
    "    df['IND1990'] == 411,\n",
    "    df['IND1990'] == 412,\n",
    "    df['IND1990'] == 420,\n",
    "    df['IND1990'] == 421,\n",
    "    df['IND1990'] == 422,\n",
    "    df['IND1990'] == 432,\n",
    "    df['IND1990'] == 440,\n",
    "    df['IND1990'] == 441,\n",
    "    df['IND1990'] == 442,\n",
    "    df['IND1990'] == 450,\n",
    "    df['IND1990'] == 451,\n",
    "    df['IND1990'] == 452,\n",
    "    df['IND1990'] == 470,\n",
    "    df['IND1990'] == 471,\n",
    "    df['IND1990'] == 472,\n",
    "    df['IND1990'] == 500,\n",
    "    df['IND1990'] == 501,\n",
    "    df['IND1990'] == 502,\n",
    "    df['IND1990'] == 510,\n",
    "    df['IND1990'] == 511,\n",
    "    df['IND1990'] == 512,\n",
    "    df['IND1990'] == 521,\n",
    "    df['IND1990'] == 530,\n",
    "    df['IND1990'] == 531,\n",
    "    df['IND1990'] == 532,\n",
    "    df['IND1990'] == 540,\n",
    "    df['IND1990'] == 541,\n",
    "    df['IND1990'] == 542,\n",
    "    df['IND1990'] == 550,\n",
    "    df['IND1990'] == 551,\n",
    "    df['IND1990'] == 552,\n",
    "    df['IND1990'] == 560,\n",
    "    df['IND1990'] == 561,\n",
    "    df['IND1990'] == 562,\n",
    "    df['IND1990'] == 571,\n",
    "    df['IND1990'] == 580,\n",
    "    df['IND1990'] == 581,\n",
    "    df['IND1990'] == 582,\n",
    "    df['IND1990'] == 590,\n",
    "    df['IND1990'] == 591,\n",
    "    df['IND1990'] == 592,\n",
    "    df['IND1990'] == 600,\n",
    "    df['IND1990'] == 601,\n",
    "    df['IND1990'] == 602,\n",
    "    df['IND1990'] == 610,\n",
    "    df['IND1990'] == 611,\n",
    "    df['IND1990'] == 612,\n",
    "    df['IND1990'] == 620,\n",
    "    df['IND1990'] == 621,\n",
    "    df['IND1990'] == 622,\n",
    "    df['IND1990'] == 623,\n",
    "    df['IND1990'] == 630,\n",
    "    df['IND1990'] == 631,\n",
    "    df['IND1990'] == 632,\n",
    "    df['IND1990'] == 633,\n",
    "    df['IND1990'] == 640,\n",
    "    df['IND1990'] == 641,\n",
    "    df['IND1990'] == 642,\n",
    "    df['IND1990'] == 650,\n",
    "    df['IND1990'] == 651,\n",
    "    df['IND1990'] == 652,\n",
    "    df['IND1990'] == 660,\n",
    "    df['IND1990'] == 661,\n",
    "    df['IND1990'] == 662,\n",
    "    df['IND1990'] == 663,\n",
    "    df['IND1990'] == 670,\n",
    "    df['IND1990'] == 671,\n",
    "    df['IND1990'] == 672,\n",
    "    df['IND1990'] == 681,\n",
    "    df['IND1990'] == 682,\n",
    "    df['IND1990'] == 691,\n",
    "    df['IND1990'] == 700,\n",
    "    df['IND1990'] == 701,\n",
    "    df['IND1990'] == 702,\n",
    "    df['IND1990'] == 710,\n",
    "    df['IND1990'] == 711,\n",
    "    df['IND1990'] == 712,\n",
    "    df['IND1990'] == 721,\n",
    "    df['IND1990'] == 722,\n",
    "    df['IND1990'] == 731,\n",
    "    df['IND1990'] == 732,\n",
    "    df['IND1990'] == 740,\n",
    "    df['IND1990'] == 741,\n",
    "    df['IND1990'] == 742,\n",
    "    df['IND1990'] == 750,\n",
    "    df['IND1990'] == 751,\n",
    "    df['IND1990'] == 752,\n",
    "    df['IND1990'] == 760,\n",
    "    df['IND1990'] == 761,\n",
    "    df['IND1990'] == 762,\n",
    "    df['IND1990'] == 770,\n",
    "    df['IND1990'] == 771,\n",
    "    df['IND1990'] == 772,\n",
    "    df['IND1990'] == 780,\n",
    "    df['IND1990'] == 781,\n",
    "    df['IND1990'] == 782,\n",
    "    df['IND1990'] == 790,\n",
    "    df['IND1990'] == 791,\n",
    "    df['IND1990'] == 800,\n",
    "    df['IND1990'] == 801,\n",
    "    df['IND1990'] == 802,\n",
    "    df['IND1990'] == 810,\n",
    "    df['IND1990'] == 812,\n",
    "    df['IND1990'] == 820,\n",
    "    df['IND1990'] == 821,\n",
    "    df['IND1990'] == 822,\n",
    "    df['IND1990'] == 830,\n",
    "    df['IND1990'] == 831,\n",
    "    df['IND1990'] == 832,\n",
    "    df['IND1990'] == 840,\n",
    "    df['IND1990'] == 841,\n",
    "    df['IND1990'] == 842,\n",
    "    df['IND1990'] == 850,\n",
    "    df['IND1990'] == 851,\n",
    "    df['IND1990'] == 852,\n",
    "    df['IND1990'] == 860,\n",
    "    df['IND1990'] == 861,\n",
    "    df['IND1990'] == 862,\n",
    "    df['IND1990'] == 863,\n",
    "    df['IND1990'] == 870,\n",
    "    df['IND1990'] == 871,\n",
    "    df['IND1990'] == 872,\n",
    "    df['IND1990'] == 873,\n",
    "    df['IND1990'] == 880,\n",
    "    df['IND1990'] == 881,\n",
    "    df['IND1990'] == 882,\n",
    "    df['IND1990'] == 890,\n",
    "    df['IND1990'] == 891,\n",
    "    df['IND1990'] == 892,\n",
    "    df['IND1990'] == 893,\n",
    "    df['IND1990'] == 900,\n",
    "    df['IND1990'] == 901,\n",
    "    df['IND1990'] == 910,\n",
    "    df['IND1990'] == 921,\n",
    "    df['IND1990'] == 922,\n",
    "    df['IND1990'] == 930,\n",
    "    df['IND1990'] == 931,\n",
    "    df['IND1990'] == 932,\n",
    "    df['IND1990'] == 940,\n",
    "    df['IND1990'] == 941,\n",
    "    df['IND1990'] == 942,\n",
    "    df['IND1990'] == 950,\n",
    "    df['IND1990'] == 951,\n",
    "    df['IND1990'] == 952,\n",
    "    df['IND1990'] == 960,\n",
    "    df['IND1990'] == 998    \n",
    "]\n",
    "\n",
    "choices = [\n",
    "'NIU',\n",
    "'AGRICULTURE, FORESTRY, AND FISHERIES',\n",
    "'AGRICULTURE, FORESTRY, AND FISHERIES',\n",
    "'AGRICULTURE, FORESTRY, AND FISHERIES',\n",
    "'AGRICULTURE, FORESTRY, AND FISHERIES',\n",
    "'AGRICULTURE, FORESTRY, AND FISHERIES',\n",
    "'AGRICULTURE, FORESTRY, AND FISHERIES',\n",
    "'AGRICULTURE, FORESTRY, AND FISHERIES',\n",
    "'MINING',\n",
    "'MINING',\n",
    "'MINING',\n",
    "'MINING',\n",
    "'CONSTRUCTION',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'MANUFACTURING',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'TRANSPORTATION, COMMUNICATIONS, AND OTHER PUBLIC UTILITIES',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'WHOLESALE TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'RETAIL TRADE',\n",
    "'FINANCE, INSURANCE, AND REAL ESTATE',\n",
    "'FINANCE, INSURANCE, AND REAL ESTATE',\n",
    "'FINANCE, INSURANCE, AND REAL ESTATE',\n",
    "'FINANCE, INSURANCE, AND REAL ESTATE',\n",
    "'FINANCE, INSURANCE, AND REAL ESTATE',\n",
    "'FINANCE, INSURANCE, AND REAL ESTATE',\n",
    "'BUSINESS AND REPAIR SERVICES',\n",
    "'BUSINESS AND REPAIR SERVICES',\n",
    "'BUSINESS AND REPAIR SERVICES',\n",
    "'BUSINESS AND REPAIR SERVICES',\n",
    "'BUSINESS AND REPAIR SERVICES',\n",
    "'BUSINESS AND REPAIR SERVICES',\n",
    "'BUSINESS AND REPAIR SERVICES',\n",
    "'BUSINESS AND REPAIR SERVICES',\n",
    "'BUSINESS AND REPAIR SERVICES',\n",
    "'BUSINESS AND REPAIR SERVICES',\n",
    "'BUSINESS AND REPAIR SERVICES',\n",
    "'PERSONAL SERVICES',\n",
    "'PERSONAL SERVICES',\n",
    "'PERSONAL SERVICES',\n",
    "'PERSONAL SERVICES',\n",
    "'PERSONAL SERVICES',\n",
    "'PERSONAL SERVICES',\n",
    "'PERSONAL SERVICES',\n",
    "'PERSONAL SERVICES',\n",
    "'PERSONAL SERVICES',\n",
    "'PERSONAL SERVICES',\n",
    "'ENTERTAINMENT AND RECREATION SERVICES',\n",
    "'ENTERTAINMENT AND RECREATION SERVICES',\n",
    "'ENTERTAINMENT AND RECREATION SERVICES',\n",
    "'ENTERTAINMENT AND RECREATION SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PROFESSIONAL AND RELATED SERVICES',\n",
    "'PUBLIC ADMINISTRATION',\n",
    "'PUBLIC ADMINISTRATION',\n",
    "'PUBLIC ADMINISTRATION',\n",
    "'PUBLIC ADMINISTRATION',\n",
    "'PUBLIC ADMINISTRATION',\n",
    "'PUBLIC ADMINISTRATION',\n",
    "'PUBLIC ADMINISTRATION',\n",
    "'PUBLIC ADMINISTRATION',\n",
    "'ACTIVE DUTY MILITARY',\n",
    "'ACTIVE DUTY MILITARY',\n",
    "'ACTIVE DUTY MILITARY',\n",
    "'ACTIVE DUTY MILITARY',\n",
    "'ACTIVE DUTY MILITARY',\n",
    "'ACTIVE DUTY MILITARY',\n",
    "'ACTIVE DUTY MILITARY',\n",
    "'UNKNOWN'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['IND1990'] = np.select(conditions, choices, df['IND1990'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GET RID OF UNREPORTED/ZERO WORK HOURS\n",
    "df = df[(df['UHRSWORK1'] != 999) & (df['UHRSWORK1'] > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIND MEDIAN WORK HOURS TO IMPUTE ON VARIED WORKERS\n",
    "MEDIAN = 39 #ROUND TO CLOSEST INT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['UHRSWORK1'] = np.where(df['UHRSWORK1'] == 997, MEDIAN, df['UHRSWORK1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['AGE'] > 17)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA/HYPOTHESIS TESTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a236c1208>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot('INCTOT', data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RACE VS TARGET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>INCTOT</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RACE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>african-american</td>\n",
       "      <td>7032</td>\n",
       "      <td>1692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>asian</td>\n",
       "      <td>1684</td>\n",
       "      <td>1210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>eskimo</td>\n",
       "      <td>632</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hawaiian</td>\n",
       "      <td>93</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mixed</td>\n",
       "      <td>848</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>white</td>\n",
       "      <td>36834</td>\n",
       "      <td>16415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "INCTOT                0      1\n",
       "RACE                          \n",
       "african-american   7032   1692\n",
       "asian              1684   1210\n",
       "eskimo              632    137\n",
       "hawaiian             93     23\n",
       "mixed               848    211\n",
       "white             36834  16415"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CREATE DF FOR CHI SQUARE\n",
    "df_both = df.groupby('RACE')['INCTOT'].value_counts()\n",
    "df_new = df_both.unstack()\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.15319411e+03 2.57080589e+03]\n",
      " [2.04119025e+03 8.52809747e+02]\n",
      " [5.42389532e+02 2.26610468e+02]\n",
      " [8.18168864e+01 3.41831136e+01]\n",
      " [7.46931748e+02 3.12068252e+02]\n",
      " [3.75574775e+04 1.56915225e+04]]\n",
      "probability=0.950, critical=11.070, stat=787.163)\n",
      "Dependent (reject H0)\n",
      "signigicance=0.050, p=0.000,\n",
      "Dependent (reject H0)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import chi2\n",
    "# CHI SQUARE TEST\n",
    "stat, p, dof, expected = chi2_contingency(df_new)\n",
    "print(expected)\n",
    "\n",
    "#interpret test-statistic\n",
    "prob = 0.95\n",
    "critical = chi2.ppf(prob, dof)\n",
    "print('probability=%.3f, critical=%.3f, stat=%.3f)' % (prob, critical, stat))\n",
    "if abs(stat) >= critical:\n",
    "    print('Dependent (reject H0)')\n",
    "else:\n",
    "    print('Independent (fail to reject H0)')\n",
    "#interpret p-value\n",
    "alpha= 1-prob\n",
    "print('signigicance=%.3f, p=%.3f,' % (alpha, p))\n",
    "if p <= alpha:\n",
    "    print('Dependent (reject H0)')\n",
    "else: \n",
    "    print('Independent (fail to reject)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Difference of Race on Income Total')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_new.plot(kind='bar', stacked = True)\n",
    "plt.title('Difference of Race on Income Total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DUMMIES FOR POST-HOC\n",
    "dummies = pd.get_dummies(df['RACE'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INCTOT      0      1\n",
      "asian               \n",
      "0       45439  18478\n",
      "1        1684   1210 \n",
      "\n",
      "Chi2 value= 221.09400100586893\n",
      "p-value= 5.221036215128847e-50\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT      0      1\n",
      "eskimo              \n",
      "0       46491  19551\n",
      "1         632    137 \n",
      "\n",
      "Chi2 value= 50.25974493320464\n",
      "p-value= 1.3468421641119533e-12\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT        0      1\n",
      "hawaiian              \n",
      "0         47030  19665\n",
      "1            93     23 \n",
      "\n",
      "Chi2 value= 4.741915472484279\n",
      "p-value= 0.029436279318691354\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT      0      1\n",
      "mixed               \n",
      "0       46275  19477\n",
      "1         848    211 \n",
      "\n",
      "Chi2 value= 46.690256761636945\n",
      "p-value= 8.314135173135131e-12\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT      0      1\n",
      "white               \n",
      "0       10289   3273\n",
      "1       36834  16415 \n",
      "\n",
      "Chi2 value= 232.6610646574323\n",
      "p-value= 1.5668338422485523e-52\n",
      "Degrees of freedom= 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# POST HOC TEST\n",
    "for series in dummies:\n",
    "    nl = \"\\n\"\n",
    "    \n",
    "    crosstab = pd.crosstab(dummies[f\"{series}\"], df['INCTOT'])\n",
    "    print(crosstab, nl)\n",
    "    chi2, p, dof, expected = stats.chi2_contingency(crosstab)\n",
    "    print(f\"Chi2 value= {chi2}{nl}p-value= {p}{nl}Degrees of freedom= {dof}{nl}\")\n",
    "    \n",
    "   # SIGNIFICANTLY HIGHER PROPORTION OF WHITE ETHNICITY MAKE OVER 50K COMPARED TO OTHER RACES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RELATE VS TARGET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>INCTOT</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RELATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>child</td>\n",
       "      <td>6702</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>grandchild</td>\n",
       "      <td>319</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>head</td>\n",
       "      <td>22471</td>\n",
       "      <td>11854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>other nonrelative</td>\n",
       "      <td>409</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>other relative</td>\n",
       "      <td>770</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>parent</td>\n",
       "      <td>787</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>roommate</td>\n",
       "      <td>1235</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sibling</td>\n",
       "      <td>676</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>spouse</td>\n",
       "      <td>11686</td>\n",
       "      <td>6356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unmarried partner</td>\n",
       "      <td>2068</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "INCTOT                 0      1\n",
       "RELATE                         \n",
       "child               6702    354\n",
       "grandchild           319     13\n",
       "head               22471  11854\n",
       "other nonrelative    409     64\n",
       "other relative       770     75\n",
       "parent               787    165\n",
       "roommate            1235    223\n",
       "sibling              676     54\n",
       "spouse             11686   6356\n",
       "unmarried partner   2068    530"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CREATE DF FOR CHI SQUARE\n",
    "dfR = df.groupby('RELATE')['INCTOT'].value_counts()\n",
    "dfRI = dfR.unstack()\n",
    "dfRI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.97663878e+03 2.07924080e+03 1.70587932e-01]\n",
      " [2.34187528e+02 9.78436017e+01 8.02741927e-03]\n",
      " [2.42096982e+04 1.01148173e+04 8.29853749e-01]\n",
      " [3.33702774e+02 1.39421094e+02 1.14385771e-02]\n",
      " [5.96041891e+02 2.49026436e+02 2.04309692e-02]\n",
      " [6.71568732e+02 2.80581567e+02 2.30198587e-02]\n",
      " [1.02843664e+03 4.29681059e+02 3.52524841e-02]\n",
      " [5.14921855e+02 2.15134467e+02 1.76503577e-02]\n",
      " [1.27252879e+04 5.31662816e+03 4.36194116e-01]\n",
      " [1.83251571e+03 7.65625477e+02 6.28144979e-02]]\n",
      "probability=0.950, critical=28.869, stat=3562.281)\n",
      "Dependent (reject H0)\n",
      "signigicance=0.050, p=0.000,\n",
      "Dependent (reject H0)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import chi2\n",
    "# CHI SQUARE TEST\n",
    "stat, p, dof, expected = chi2_contingency(dfRI)\n",
    "print(expected)\n",
    "\n",
    "#interpret test-statistic\n",
    "prob = 0.95\n",
    "critical = chi2.ppf(prob, dof)\n",
    "print('probability=%.3f, critical=%.3f, stat=%.3f)' % (prob, critical, stat))\n",
    "if abs(stat) >= critical:\n",
    "    print('Dependent (reject H0)')\n",
    "else:\n",
    "    print('Independent (fail to reject H0)')\n",
    "#interpret p-value\n",
    "alpha= 1-prob\n",
    "print('signigicance=%.3f, p=%.3f,' % (alpha, p))\n",
    "if p <= alpha:\n",
    "    print('Dependent (reject H0)')\n",
    "else: \n",
    "    print('Independent (fail to reject)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfRI.plot(kind='bar', stacked = True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RELATE\n",
       "child                 6702\n",
       "grandchild             319\n",
       "head                 22471\n",
       "other nonrelative      409\n",
       "other relative         770\n",
       "parent                 787\n",
       "roommate              1235\n",
       "sibling                676\n",
       "spouse               11686\n",
       "unmarried partner     2068\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfRI[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfRI['percentage_>median'] = dfRI[1]/(dfRI[0]+dfRI[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a236dff60>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfRI['percentage_>median'].plot(kind='bar', stacked=True, figsize = (14,8))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# DUMMIES FOR POST-HOC\n",
    "dummies = pd.get_dummies(df['RELATE'],drop_first=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INCTOT          0      1\n",
      "grandchild              \n",
      "0           46804  19675\n",
      "1             319     13 \n",
      "\n",
      "Chi2 value= 103.58494383158802\n",
      "p-value= 2.494658033774648e-24\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT      0      1\n",
      "head                \n",
      "0       24652   7834\n",
      "1       22471  11854 \n",
      "\n",
      "Chi2 value= 871.3084334112878\n",
      "p-value= 1.6948635985343722e-191\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                 0      1\n",
      "other nonrelative              \n",
      "0                  46714  19624\n",
      "1                    409     64 \n",
      "\n",
      "Chi2 value= 57.44745713936496\n",
      "p-value= 3.4712969320511024e-14\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT              0      1\n",
      "other relative              \n",
      "0               46353  19613\n",
      "1                 770     75 \n",
      "\n",
      "Chi2 value= 173.60531979647317\n",
      "p-value= 1.2071903630167e-39\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT      0      1\n",
      "parent              \n",
      "0       46336  19523\n",
      "1         787    165 \n",
      "\n",
      "Chi2 value= 67.84759984136457\n",
      "p-value= 1.7663346321791814e-16\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT        0      1\n",
      "roommate              \n",
      "0         45888  19465\n",
      "1          1235    223 \n",
      "\n",
      "Chi2 value= 143.36321747144177\n",
      "p-value= 4.89571161743127e-33\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT       0      1\n",
      "sibling              \n",
      "0        46447  19634\n",
      "1          676     54 \n",
      "\n",
      "Chi2 value= 171.908615030776\n",
      "p-value= 2.833473839655587e-39\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT      0      1\n",
      "spouse              \n",
      "0       35437  13332\n",
      "1       11686   6356 \n",
      "\n",
      "Chi2 value= 394.2612824920202\n",
      "p-value= 9.776955722241206e-88\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                 0      1\n",
      "unmarried partner              \n",
      "0                  45055  19158\n",
      "1                   2068    530 \n",
      "\n",
      "Chi2 value= 106.48610547962564\n",
      "p-value= 5.769560862382308e-25\n",
      "Degrees of freedom= 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# POST HOC TEST\n",
    "for series in dummies:\n",
    "    nl = \"\\n\"\n",
    "    \n",
    "    crosstab = pd.crosstab(dummies[f\"{series}\"], df['INCTOT'])\n",
    "    print(crosstab, nl)\n",
    "    chi2, p, dof, expected = stats.chi2_contingency(crosstab)\n",
    "    print(f\"Chi2 value= {chi2}{nl}p-value= {p}{nl}Degrees of freedom= {dof}{nl}\")\n",
    "    \n",
    "# HEAD OF HOUSEHOLD MAKES MORE THAN 50K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FAMREL VS TARGET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>INCTOT</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAMREL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>child</td>\n",
       "      <td>6090</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>not-family-member</td>\n",
       "      <td>10133</td>\n",
       "      <td>3592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>other-relative</td>\n",
       "      <td>1643</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>reference-person</td>\n",
       "      <td>17131</td>\n",
       "      <td>9195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>spouse</td>\n",
       "      <td>12126</td>\n",
       "      <td>6483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "INCTOT                 0     1\n",
       "FAMREL                        \n",
       "child               6090   285\n",
       "not-family-member  10133  3592\n",
       "other-relative      1643   133\n",
       "reference-person   17131  9195\n",
       "spouse             12126  6483"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CREATE DF FOR CHI SQUARE\n",
    "dff = df.groupby('FAMREL')['INCTOT'].value_counts()\n",
    "dffi = dff.unstack()\n",
    "dffi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4496.40216431  1878.59783569]\n",
      " [ 9680.48936552  4044.51063448]\n",
      " [ 1252.64474413   523.35525587]\n",
      " [18568.20131415  7757.79868585]\n",
      " [13125.26241188  5483.73758812]]\n",
      "probability=0.950, critical=9.488, stat=3036.872)\n",
      "Dependent (reject H0)\n",
      "signigicance=0.050, p=0.000,\n",
      "Dependent (reject H0)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import chi2\n",
    "# CHI SQUARE TEST\n",
    "stat, p, dof, expected = chi2_contingency(dffi)\n",
    "print(expected)\n",
    "\n",
    "#interpret test-statistic\n",
    "prob = 0.95\n",
    "critical = chi2.ppf(prob, dof)\n",
    "print('probability=%.3f, critical=%.3f, stat=%.3f)' % (prob, critical, stat))\n",
    "if abs(stat) >= critical:\n",
    "    print('Dependent (reject H0)')\n",
    "else:\n",
    "    print('Independent (fail to reject H0)')\n",
    "#interpret p-value\n",
    "alpha= 1-prob\n",
    "print('signigicance=%.3f, p=%.3f,' % (alpha, p))\n",
    "if p <= alpha:\n",
    "    print('Dependent (reject H0)')\n",
    "else: \n",
    "    print('Independent (fail to reject)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1fbba9e8>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "dffi.plot(kind='bar', stacked = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# DUMMIES FOR POST-HOC\n",
    "dummies = pd.get_dummies(df['FAMREL'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INCTOT                 0      1\n",
      "not-family-member              \n",
      "0                  36990  16096\n",
      "1                  10133   3592 \n",
      "\n",
      "Chi2 value= 90.1393317018373\n",
      "p-value= 2.2196501087123138e-21\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT              0      1\n",
      "other-relative              \n",
      "0               45480  19555\n",
      "1                1643    133 \n",
      "\n",
      "Chi2 value= 422.98607044446237\n",
      "p-value= 5.463842031849005e-94\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                0      1\n",
      "reference-person              \n",
      "0                 29992  10493\n",
      "1                 17131   9195 \n",
      "\n",
      "Chi2 value= 622.5343817946083\n",
      "p-value= 2.1015520101198394e-137\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT      0      1\n",
      "spouse              \n",
      "0       34997  13205\n",
      "1       12126   6483 \n",
      "\n",
      "Chi2 value= 357.4749953154716\n",
      "p-value= 9.986234765909915e-80\n",
      "Degrees of freedom= 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#POST HOC TEST\n",
    "for series in dummies:\n",
    "    nl = \"\\n\"\n",
    "    \n",
    "    crosstab = pd.crosstab(dummies[f\"{series}\"], df['INCTOT'])\n",
    "    print(crosstab, nl)\n",
    "    chi2, p, dof, expected = stats.chi2_contingency(crosstab)\n",
    "    print(f\"Chi2 value= {chi2}{nl}p-value= {p}{nl}Degrees of freedom= {dof}{nl}\")\n",
    "    \n",
    "# SIGNIFICANT DIFFERENCES AMONGST ALL GROUPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FAMREL             INCTOT\n",
       "child              0          6090\n",
       "                   1           285\n",
       "not-family-member  0         10133\n",
       "                   1          3592\n",
       "other-relative     0          1643\n",
       "                   1           133\n",
       "reference-person   0         17131\n",
       "                   1          9195\n",
       "spouse             0         12126\n",
       "                   1          6483\n",
       "Name: INCTOT, dtype: int64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('FAMREL').INCTOT.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDUCATION VS. TARGET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "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>INCTOT</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUC</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>12th grade, no diploma</td>\n",
       "      <td>749</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Associate's degree, academic program</td>\n",
       "      <td>3041</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Associate's degree, occupational/vocational program</td>\n",
       "      <td>2406</td>\n",
       "      <td>843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bachelor's degree</td>\n",
       "      <td>8924</td>\n",
       "      <td>6722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Doctorate degree</td>\n",
       "      <td>351</td>\n",
       "      <td>1082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Grade 10</td>\n",
       "      <td>1072</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Grade 11</td>\n",
       "      <td>1511</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Grade 9</td>\n",
       "      <td>782</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Grades 1, 2, 3, or 4</td>\n",
       "      <td>202</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Grades 5 or 6</td>\n",
       "      <td>575</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Grades 7 or 8</td>\n",
       "      <td>562</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>High school diploma or equivalent</td>\n",
       "      <td>14850</td>\n",
       "      <td>2739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Master's degree</td>\n",
       "      <td>2733</td>\n",
       "      <td>3937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>None or preschool</td>\n",
       "      <td>96</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Professional school degree</td>\n",
       "      <td>283</td>\n",
       "      <td>846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Some college but no degree</td>\n",
       "      <td>8986</td>\n",
       "      <td>2159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "INCTOT                                                  0     1\n",
       "EDUC                                                           \n",
       "12th grade, no diploma                                749    53\n",
       "Associate's degree, academic program                 3041   998\n",
       "Associate's degree, occupational/vocational pro...   2406   843\n",
       "Bachelor's degree                                    8924  6722\n",
       "Doctorate degree                                      351  1082\n",
       "Grade 10                                             1072    64\n",
       "Grade 11                                             1511    93\n",
       "Grade 9                                               782    52\n",
       "Grades 1, 2, 3, or 4                                  202    11\n",
       "Grades 5 or 6                                         575    31\n",
       "Grades 7 or 8                                         562    53\n",
       "High school diploma or equivalent                   14850  2739\n",
       "Master's degree                                      2733  3937\n",
       "None or preschool                                      96     5\n",
       "Professional school degree                            283   846\n",
       "Some college but no degree                           8986  2159"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CREATE DF FOR CHI SQUARE\n",
    "dfe = dff = df.groupby('EDUC')['INCTOT'].value_counts()\n",
    "dfei = dfe.unstack()\n",
    "dfei"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  565.66502522   236.33497478]\n",
      " [ 2848.77934771  1190.22065229]\n",
      " [ 2291.57813833   957.42186167]\n",
      " [11035.4052177   4610.5947823 ]\n",
      " [ 1010.72067474   422.27932526]\n",
      " [  801.24123273   334.75876727]\n",
      " [ 1131.33005044   472.66994956]\n",
      " [  588.23520079   245.76479921]\n",
      " [  150.23273114    62.76726886]\n",
      " [  427.42269985   178.57730015]\n",
      " [  433.77056173   181.22943827]\n",
      " [12405.83806559  5183.16193441]\n",
      " [ 4704.47097035  1965.52902965]\n",
      " [   71.23711664    29.76288336]\n",
      " [  796.30400683   332.69599317]\n",
      " [ 7860.7689602   3284.2310398 ]]\n",
      "probability=0.950, critical=24.996, stat=10555.114)\n",
      "Dependent (reject H0)\n",
      "signigicance=0.050, p=0.000,\n",
      "Dependent (reject H0)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import chi2\n",
    "# CHI SQUARE TEST\n",
    "stat, p, dof, expected = chi2_contingency(dfei)\n",
    "print(expected)\n",
    "\n",
    "#interpret test-statistic\n",
    "prob = 0.95\n",
    "critical = chi2.ppf(prob, dof)\n",
    "print('probability=%.3f, critical=%.3f, stat=%.3f)' % (prob, critical, stat))\n",
    "if abs(stat) >= critical:\n",
    "    print('Dependent (reject H0)')\n",
    "else:\n",
    "    print('Independent (fail to reject H0)')\n",
    "#interpret p-value\n",
    "alpha= 1-prob\n",
    "print('signigicance=%.3f, p=%.3f,' % (alpha, p))\n",
    "if p <= alpha:\n",
    "    print('Dependent (reject H0)')\n",
    "else: \n",
    "    print('Independent (fail to reject)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Associate's degree, academic program</th>\n",
       "      <th>Associate's degree, occupational/vocational program</th>\n",
       "      <th>Bachelor's degree</th>\n",
       "      <th>Doctorate degree</th>\n",
       "      <th>Grade 10</th>\n",
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       "      <th>High school diploma or equivalent</th>\n",
       "      <th>Master's degree</th>\n",
       "      <th>None or preschool</th>\n",
       "      <th>Professional school degree</th>\n",
       "      <th>Some college but no degree</th>\n",
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       "  </thead>\n",
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      ],
      "text/plain": [
       "   Associate's degree, academic program  \\\n",
       "1                                     0   \n",
       "2                                     0   \n",
       "6                                     0   \n",
       "7                                     0   \n",
       "8                                     0   \n",
       "\n",
       "   Associate's degree, occupational/vocational program  Bachelor's degree  \\\n",
       "1                                                  1                    0   \n",
       "2                                                  0                    0   \n",
       "6                                                  0                    0   \n",
       "7                                                  0                    0   \n",
       "8                                                  0                    0   \n",
       "\n",
       "   Doctorate degree  Grade 10  Grade 11  Grade 9  Grades 1, 2, 3, or 4  \\\n",
       "1                 0         0         0        0                     0   \n",
       "2                 0         0         0        0                     0   \n",
       "6                 0         0         0        0                     0   \n",
       "7                 0         0         0        0                     0   \n",
       "8                 0         0         0        0                     0   \n",
       "\n",
       "   Grades 5 or 6  Grades 7 or 8  High school diploma or equivalent  \\\n",
       "1              0              0                                  0   \n",
       "2              0              0                                  0   \n",
       "6              0              1                                  0   \n",
       "7              0              1                                  0   \n",
       "8              0              1                                  0   \n",
       "\n",
       "   Master's degree  None or preschool  Professional school degree  \\\n",
       "1                0                  0                           0   \n",
       "2                1                  0                           0   \n",
       "6                0                  0                           0   \n",
       "7                0                  0                           0   \n",
       "8                0                  0                           0   \n",
       "\n",
       "   Some college but no degree  \n",
       "1                           0  \n",
       "2                           0  \n",
       "6                           0  \n",
       "7                           0  \n",
       "8                           0  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DUMMIES FOR POST-HOC\n",
    "dummies = pd.get_dummies(df['EDUC'],drop_first=True)\n",
    "dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INCTOT                                    0      1\n",
      "Associate's degree, academic program              \n",
      "0                                     44082  18690\n",
      "1                                      3041    998 \n",
      "\n",
      "Chi2 value= 46.602301052194704\n",
      "p-value= 8.69580812183319e-12\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                                                  0      1\n",
      "Associate's degree, occupational/vocational pro...              \n",
      "0                                                   44717  18845\n",
      "1                                                    2406    843 \n",
      "\n",
      "Chi2 value= 20.201158030095794\n",
      "p-value= 6.9710722457339465e-06\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                 0      1\n",
      "Bachelor's degree              \n",
      "0                  38199  12966\n",
      "1                   8924   6722 \n",
      "\n",
      "Chi2 value= 1789.2480266250932\n",
      "p-value= 0.0\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                0      1\n",
      "Doctorate degree              \n",
      "0                 46772  18606\n",
      "1                   351   1082 \n",
      "\n",
      "Chi2 value= 1491.0534163797206\n",
      "p-value= 0.0\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT        0      1\n",
      "Grade 10              \n",
      "0         46051  19624\n",
      "1          1072     64 \n",
      "\n",
      "Chi2 value= 314.6955158762432\n",
      "p-value= 2.0718700879252172e-70\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT        0      1\n",
      "Grade 11              \n",
      "0         45612  19595\n",
      "1          1511     93 \n",
      "\n",
      "Chi2 value= 441.8538283673851\n",
      "p-value= 4.2754676508493335e-98\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT       0      1\n",
      "Grade 9              \n",
      "0        46341  19636\n",
      "1          782     52 \n",
      "\n",
      "Chi2 value= 218.20077536556346\n",
      "p-value= 2.232788228506647e-49\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                    0      1\n",
      "Grades 1, 2, 3, or 4              \n",
      "0                     46921  19677\n",
      "1                       202     11 \n",
      "\n",
      "Chi2 value= 59.55921405590984\n",
      "p-value= 1.1866923284357692e-14\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT             0      1\n",
      "Grades 5 or 6              \n",
      "0              46548  19657\n",
      "1                575     31 \n",
      "\n",
      "Chi2 value= 173.31544579142212\n",
      "p-value= 1.3966253230851172e-39\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT             0      1\n",
      "Grades 7 or 8              \n",
      "0              46561  19635\n",
      "1                562     53 \n",
      "\n",
      "Chi2 value= 128.82036516129932\n",
      "p-value= 7.424251202463678e-30\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                                 0      1\n",
      "High school diploma or equivalent              \n",
      "0                                  32273  16949\n",
      "1                                  14850   2739 \n",
      "\n",
      "Chi2 value= 2217.130350243203\n",
      "p-value= 0.0\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT               0      1\n",
      "Master's degree              \n",
      "0                44390  15751\n",
      "1                 2733   3937 \n",
      "\n",
      "Chi2 value= 3112.958715569449\n",
      "p-value= 0.0\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                 0      1\n",
      "None or preschool              \n",
      "0                  47027  19683\n",
      "1                     96      5 \n",
      "\n",
      "Chi2 value= 28.085482597577794\n",
      "p-value= 1.1607330398967791e-07\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                          0      1\n",
      "Professional school degree              \n",
      "0                           46840  18842\n",
      "1                             283    846 \n",
      "\n",
      "Chi2 value= 1139.9134918771083\n",
      "p-value= 6.983196661413805e-250\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                          0      1\n",
      "Some college but no degree              \n",
      "0                           38137  17529\n",
      "1                            8986   2159 \n",
      "\n",
      "Chi2 value= 655.4456083980082\n",
      "p-value= 1.4615248003866073e-144\n",
      "Degrees of freedom= 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#POST HOC TEST\n",
    "for series in dummies:\n",
    "    nl = \"\\n\"\n",
    "    \n",
    "    crosstab = pd.crosstab(dummies[f\"{series}\"], df['INCTOT'])\n",
    "    print(crosstab, nl)\n",
    "    chi2, p, dof, expected = stats.chi2_contingency(crosstab)\n",
    "    print(f\"Chi2 value= {chi2}{nl}p-value= {p}{nl}Degrees of freedom= {dof}{nl}\")\n",
    "    \n",
    "# DOCTORATE, MASTERS, AND PROFESSIONAL SCHOOL DEGREE MAKE MORE THAN MEDIAN INCOME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EDUC                                                 INCTOT\n",
       "12th grade, no diploma                               0           749\n",
       "                                                     1            53\n",
       "Associate's degree, academic program                 0          3041\n",
       "                                                     1           998\n",
       "Associate's degree, occupational/vocational program  0          2406\n",
       "                                                     1           843\n",
       "Bachelor's degree                                    0          8924\n",
       "                                                     1          6722\n",
       "Doctorate degree                                     1          1082\n",
       "                                                     0           351\n",
       "Grade 10                                             0          1072\n",
       "                                                     1            64\n",
       "Grade 11                                             0          1511\n",
       "                                                     1            93\n",
       "Grade 9                                              0           782\n",
       "                                                     1            52\n",
       "Grades 1, 2, 3, or 4                                 0           202\n",
       "                                                     1            11\n",
       "Grades 5 or 6                                        0           575\n",
       "                                                     1            31\n",
       "Grades 7 or 8                                        0           562\n",
       "                                                     1            53\n",
       "High school diploma or equivalent                    0         14850\n",
       "                                                     1          2739\n",
       "Master's degree                                      1          3937\n",
       "                                                     0          2733\n",
       "None or preschool                                    0            96\n",
       "                                                     1             5\n",
       "Professional school degree                           1           846\n",
       "                                                     0           283\n",
       "Some college but no degree                           0          8986\n",
       "                                                     1          2159\n",
       "Name: INCTOT, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('EDUC').INCTOT.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>INCTOT</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>percentage_&gt;median</th>\n",
       "    </tr>\n",
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       "      <th>EDUC</th>\n",
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       "      <td>Doctorate degree</td>\n",
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       "      <td>1082</td>\n",
       "      <td>0.755059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Professional school degree</td>\n",
       "      <td>283</td>\n",
       "      <td>846</td>\n",
       "      <td>0.749336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Master's degree</td>\n",
       "      <td>2733</td>\n",
       "      <td>3937</td>\n",
       "      <td>0.590255</td>\n",
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       "      <td>Bachelor's degree</td>\n",
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       "      <td>0.429631</td>\n",
       "    </tr>\n",
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       "      <td>Associate's degree, occupational/vocational program</td>\n",
       "      <td>2406</td>\n",
       "      <td>843</td>\n",
       "      <td>0.259464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Associate's degree, academic program</td>\n",
       "      <td>3041</td>\n",
       "      <td>998</td>\n",
       "      <td>0.247091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Some college but no degree</td>\n",
       "      <td>8986</td>\n",
       "      <td>2159</td>\n",
       "      <td>0.193719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>High school diploma or equivalent</td>\n",
       "      <td>14850</td>\n",
       "      <td>2739</td>\n",
       "      <td>0.155722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Grades 7 or 8</td>\n",
       "      <td>562</td>\n",
       "      <td>53</td>\n",
       "      <td>0.086179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12th grade, no diploma</td>\n",
       "      <td>749</td>\n",
       "      <td>53</td>\n",
       "      <td>0.066085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Grade 9</td>\n",
       "      <td>782</td>\n",
       "      <td>52</td>\n",
       "      <td>0.062350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Grade 11</td>\n",
       "      <td>1511</td>\n",
       "      <td>93</td>\n",
       "      <td>0.057980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Grade 10</td>\n",
       "      <td>1072</td>\n",
       "      <td>64</td>\n",
       "      <td>0.056338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Grades 1, 2, 3, or 4</td>\n",
       "      <td>202</td>\n",
       "      <td>11</td>\n",
       "      <td>0.051643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Grades 5 or 6</td>\n",
       "      <td>575</td>\n",
       "      <td>31</td>\n",
       "      <td>0.051155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>None or preschool</td>\n",
       "      <td>96</td>\n",
       "      <td>5</td>\n",
       "      <td>0.049505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "INCTOT                                                  0     1  \\\n",
       "EDUC                                                              \n",
       "Doctorate degree                                      351  1082   \n",
       "Professional school degree                            283   846   \n",
       "Master's degree                                      2733  3937   \n",
       "Bachelor's degree                                    8924  6722   \n",
       "Associate's degree, occupational/vocational pro...   2406   843   \n",
       "Associate's degree, academic program                 3041   998   \n",
       "Some college but no degree                           8986  2159   \n",
       "High school diploma or equivalent                   14850  2739   \n",
       "Grades 7 or 8                                         562    53   \n",
       "12th grade, no diploma                                749    53   \n",
       "Grade 9                                               782    52   \n",
       "Grade 11                                             1511    93   \n",
       "Grade 10                                             1072    64   \n",
       "Grades 1, 2, 3, or 4                                  202    11   \n",
       "Grades 5 or 6                                         575    31   \n",
       "None or preschool                                      96     5   \n",
       "\n",
       "INCTOT                                              percentage_>median  \n",
       "EDUC                                                                    \n",
       "Doctorate degree                                              0.755059  \n",
       "Professional school degree                                    0.749336  \n",
       "Master's degree                                               0.590255  \n",
       "Bachelor's degree                                             0.429631  \n",
       "Associate's degree, occupational/vocational pro...            0.259464  \n",
       "Associate's degree, academic program                          0.247091  \n",
       "Some college but no degree                                    0.193719  \n",
       "High school diploma or equivalent                             0.155722  \n",
       "Grades 7 or 8                                                 0.086179  \n",
       "12th grade, no diploma                                        0.066085  \n",
       "Grade 9                                                       0.062350  \n",
       "Grade 11                                                      0.057980  \n",
       "Grade 10                                                      0.056338  \n",
       "Grades 1, 2, 3, or 4                                          0.051643  \n",
       "Grades 5 or 6                                                 0.051155  \n",
       "None or preschool                                             0.049505  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education = df.groupby(['EDUC', 'INCTOT'])['EDUC'].count().unstack()\n",
    "education.fillna(value = 0, inplace = True)\n",
    "education['percentage_>median'] = education[1]/(education[0]+education[1])\n",
    "education.sort_values(by='percentage_>median',ascending = False, inplace = True)\n",
    "education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BAR PLOT TO SHOW DISTRIBUTION\n",
    "\n",
    "plt.figure(figsize = (10,8))\n",
    "ax= sns.countplot(x='EDUC',hue='INCTOT',data=df, color='yellow')\n",
    "ax.set_title('Difference of Education Between Those Who Do/Do Not Make Median Income',fontdict={'fontsize':'x-large'})\n",
    "ax.set_xlabel('Education',fontdict={'fontsize':'x-large'})\n",
    "ax.set_ylabel('Count Values',fontdict={'fontsize':'x-large'})\n",
    "ax.set_xticklabels(\n",
    "    ax.get_xticklabels(), \n",
    "    rotation=60, \n",
    "    horizontalalignment='right',\n",
    "    fontweight='light',\n",
    "    fontsize='xx-large')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a235718d0>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "education['percentage_>median'].plot(kind='bar', figsize = (14,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CLASSWKR VS. TARGET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>INCTOT</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>percentage_&gt;median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLASSWKR</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Governemnt</td>\n",
       "      <td>6303</td>\n",
       "      <td>3450</td>\n",
       "      <td>0.353737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Self-Employed</td>\n",
       "      <td>4393</td>\n",
       "      <td>2275</td>\n",
       "      <td>0.341182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Private</td>\n",
       "      <td>36383</td>\n",
       "      <td>13959</td>\n",
       "      <td>0.277283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Unpaid family worker</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "INCTOT                    0      1  percentage_>median\n",
       "CLASSWKR                                              \n",
       "Governemnt             6303   3450            0.353737\n",
       "Self-Employed          4393   2275            0.341182\n",
       "Private               36383  13959            0.277283\n",
       "Unpaid family worker     44      4            0.083333"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classwrk = df.groupby(['CLASSWKR', 'INCTOT'])['CLASSWKR'].count().unstack()\n",
    "classwrk.fillna(value = 0, inplace = True)\n",
    "classwrk['percentage_>median'] = classwrk[1]/(classwrk[0]+classwrk[1])\n",
    "classwrk.sort_values(by='percentage_>median',ascending = False, inplace = True)\n",
    "classwrk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f9cba58>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classwrk['percentage_>median'].plot(kind='bar', figsize = (14,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATE DF FOR CHI SQUARE\n",
    "dfc = df.groupby('CLASSWKR')['INCTOT'].value_counts()\n",
    "dfci = dfc.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.87896632e+03 2.87403368e+03]\n",
      " [3.55071181e+04 1.48348819e+04]\n",
      " [4.70306033e+03 1.96493967e+03]\n",
      " [3.38552634e+01 1.41447366e+01]]\n",
      "probability=0.950, critical=7.815, stat=316.654)\n",
      "Dependent (reject H0)\n",
      "signigicance=0.050, p=0.000,\n",
      "Dependent (reject H0)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import chi2\n",
    "# CHI SQUARE TEST\n",
    "stat, p, dof, expected = chi2_contingency(dfci)\n",
    "print(expected)\n",
    "\n",
    "#interpret test-statistic\n",
    "prob = 0.95\n",
    "critical = chi2.ppf(prob, dof)\n",
    "print('probability=%.3f, critical=%.3f, stat=%.3f)' % (prob, critical, stat))\n",
    "if abs(stat) >= critical:\n",
    "    print('Dependent (reject H0)')\n",
    "else:\n",
    "    print('Independent (fail to reject H0)')\n",
    "#interpret p-value\n",
    "alpha= 1-prob\n",
    "print('signigicance=%.3f, p=%.3f,' % (alpha, p))\n",
    "if p <= alpha:\n",
    "    print('Dependent (reject H0)')\n",
    "else: \n",
    "    print('Independent (fail to reject)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Self-Employed</th>\n",
       "      <th>Unpaid family worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Private  Self-Employed  Unpaid family worker\n",
       "1        0              0                     0\n",
       "2        1              0                     0\n",
       "6        0              1                     0\n",
       "7        0              1                     0\n",
       "8        1              0                     0"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DUMMIES FOR POST-HOC\n",
    "dummies = pd.get_dummies(df['CLASSWKR'],drop_first=True)\n",
    "dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INCTOT       0      1\n",
      "Private              \n",
      "0        10740   5729\n",
      "1        36383  13959 \n",
      "\n",
      "Chi2 value= 297.1028619543273\n",
      "p-value= 1.4091921899893937e-66\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT             0      1\n",
      "Self-Employed              \n",
      "0              42730  17413\n",
      "1               4393   2275 \n",
      "\n",
      "Chi2 value= 76.81027502851825\n",
      "p-value= 1.881959082666455e-18\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                    0      1\n",
      "Unpaid family worker              \n",
      "0                     47079  19684\n",
      "1                        44      4 \n",
      "\n",
      "Chi2 value= 9.330674742173564\n",
      "p-value= 0.0022534936142687774\n",
      "Degrees of freedom= 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#POST HOC TEST\n",
    "for series in dummies:\n",
    "    nl = \"\\n\"\n",
    "    \n",
    "    crosstab = pd.crosstab(dummies[f\"{series}\"], df['INCTOT'])\n",
    "    print(crosstab, nl)\n",
    "    chi2, p, dof, expected = stats.chi2_contingency(crosstab)\n",
    "    print(f\"Chi2 value= {chi2}{nl}p-value= {p}{nl}Degrees of freedom= {dof}{nl}\")\n",
    "    \n",
    "# PEOPLE IN PRIVATE SECTOR MAKE MORE THAN MEDIAN INCOME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AGE VS INCTOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'age'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-201-39ad2f70eb17>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mage\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'percentage_>median'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mage\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mage\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mage\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'age'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mascending\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36msort_values\u001b[0;34m(self, by, axis, ascending, inplace, kind, na_position)\u001b[0m\n\u001b[1;32m   4991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4992\u001b[0m             \u001b[0mby\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4993\u001b[0;31m             \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4994\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4995\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_label_or_level_values\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1772\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1773\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1774\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1776\u001b[0m         \u001b[0;31m# Check for duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'age'"
     ]
    }
   ],
   "source": [
    "age = df.groupby(['AGE', 'INCTOT'])['AGE'].count().unstack()\n",
    "age.fillna(value = 0, inplace = True)\n",
    "age['percentage_>median'] = age[1]/(age[0]+age[1])\n",
    "age.sort_values(by='age',ascending = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DISTRIBUTION OF AGE\n",
    "age['percentage_>median'].plot(kind='bar', figsize = (14,8));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATING CLASSES FOR T-TEST\n",
    "class_0 = df.loc[df['INCTOT'] == 0]\n",
    "class_1 = df.loc[df['INCTOT'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40.422978163529486, 46.9373222267371)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CALCULATING AVERAGE AGES\n",
    "age1 = df.loc[df['INCTOT']==0]['AGE'].mean()\n",
    "age2 = df.loc[df['INCTOT']==1]['AGE'].mean()\n",
    "age1,age2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-59.515059643259235, pvalue=0.0)\n",
      "WE REJECT THE NULL HYPOTHESIS SINCE OUR P-VALUE IS LESS THAN 0.05. THOSE THAT DO MAKE MORE THAN 50K ARE SIGNIFICANTLY OLDER\n"
     ]
    }
   ],
   "source": [
    "# TWO SAMPLE T-TEST BETWEEN DEFAULT ON TARGET\n",
    "print(stats.ttest_ind(class_0['AGE'], class_1['AGE'], equal_var=False))\n",
    "print('WE REJECT THE NULL HYPOTHESIS SINCE OUR P-VALUE IS LESS THAN 0.05. THOSE THAT DO MAKE MORE THAN 50K ARE SIGNIFICANTLY OLDER')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col = 'INCTOT')\n",
    "g.map(plt.hist, 'AGE');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V7dla+3GuP18udR7dXm6V4YPm5iSrq+rPMnwAvFHGFvlHJ/n18fnksKWOj0WPrUXqvyrJ5VW1Jsn/e2NrX6Y3ZTgWHzLWv6aq7rIdj6Mb+zpaMQSvG+dfq+rKDCn8RUn+Ngt/Kk2SQzLcP3JVhk/wr2mtnTwOe3mSF9fQdPrcrVj+mzPcq/DNDDct/lEyfMsyyf/IcE/BhRlawCYvwb1j/PvtmvfbRaN/GOf90Qw3Of4gyfqtqGvS+nH552Z4A/3ncf5bNJ4wHpLhptBvTjxOy3D574jW2rcyXIp8VYZwdGiGb7z9MElaa+9J8sokb6uhyf1LueFXuhdyeVVdlSFI3T/jSXIMH4/OEP6+lqEV6I0ZLkEkC2zT8U389Ayfmn80Dv9kkq+38Sc0ljHfo5K8L8OllSsztPj8/FLbbwq2tB/vk+TT43Z7X5INrbWvjZ9uH57kSRk+9X4zw/7Y0k+CnJLhssFkq/GpY79l/4zE2ALyvzK0YF2c4ZP4k5Y57cbW2k9dMmmtfTlDC+YnMxwfd89wOXrOG5J8MMOXMk7P8KWUSU/NcMn2yxkC6jsztIAsVc/VrbUPLRQ6t3Y9t2GfPDnDPXAXJXlPhnuwTlyq5nmen+Fy0dzjI8uo43eTnDe+bp+Z4Wb3ZMvn0e3lgxnuD/2vDJfEfpBtv6R6A621M8cPYAvZ0vGx1LE16S8yfFnhigwfTrc07nbTWvtMhve/V4/LPiXXt8Zuj+Nozja9jlaSuW/VwQ5vbG26IMlTWmsnzboeAJhPixc7tKr6laraa7zM8sIM92Es9z4gAOhK8GJHd/8M36T5VoZLdo/divuAAKArlxoBADrR4gUA0MkO8Tte++yzTzv44INnXQYAwJJOO+20b7XW9l1o2A4RvA4++OBs3Lhx6REBAGasqhb97wIuNQIAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdLJ61gUAwEp2+OGHX/f85JNPnlkd7By0eAEAdCJ4AcAiJlu7FuqGreVS403M0UcfnU2bNs26jKm48MILkyRr1qyZcSXTs3bt2qxfv37WZQCwjQQvdhpXX331rEsAgC0SvG5idubWkg0bNiRJjjrqqBlXAgALc48XAEAnghcALGL+z0f4OQluLMELAKAT93gBwBZo5WJ70uIFANCJ4AUA0IngBQDQieAFANCJ4AUA0IngBQDQieAFANCJ4AUA0IngBQDQieAFANCJ4AUA0IngBQDQieAFANCJ4AUA0IngBQDQieAFANCJ4AUA0IngBQDQieAFANCJ4AUA0IngBQDQieAFANCJ4AUA0IngBQDQieAFANCJ4AUA0MlUg1dVPaeqzqyqL1XVW6tq16q6fVV9uqrOqaq3V9XNp1kDAMBKMbXgVVVrkvxRknWttbslWZXkSUlemeTVrbVDknwnyZHTqgEAYCVZ3WH+u1XVj5PcIsnFSR6c5LfH4ccleUmS1065DgCm6Oijj86mTZtmXcZUXHjhhUmSNWvWzLiS6Vm7dm3Wr18/6zJuEqbW4tVauzDJ/0lyfobAdUWS05Jc3lq7ZhztgiQLHslV9Yyq2lhVGzdv3jytMgFgi66++upcffXVsy6DncTUWryqau8kj0ly+ySXJ3lHkkcuMGpbaPrW2jFJjkmSdevWLTgOACvDztxasmHDhiTJUUcdNeNK2BlM8+b6hyb5Wmttc2vtx0neneQXkuxVVXOB78AkF02xBgCAFWOawev8JPerqltUVSV5SJIvJzkpyW+O4xyR5L1TrAEAYMWY5j1en07yziSnJ/niuKxjkjwvyR9X1aYkP5vkTdOqAQBgJZnqtxpba3+e5M/n9T43yX2nuVwAgJXIL9cDAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdDK14FVVd66qMyYe362qZ1fVravqxKo6Z/y797RqAABYSaYWvFprX2mtHdZaOyzJf0vy/STvSfL8JB9urR2S5MNjNwDATq/XpcaHJPlqa+3rSR6T5Lix/3FJHtupBgCAmeoVvJ6U5K3j8/1aaxcnyfj3NgtNUFXPqKqNVbVx8+bNncoEAJieqQevqrp5kl9P8o6tma61dkxrbV1rbd2+++47neIAADrq0eL1yCSnt9YuGbsvqar9k2T8e2mHGgAAZq5H8Hpyrr/MmCTvS3LE+PyIJO/tUAMAwMxNNXhV1S2SPCzJuyd6vyLJw6rqnHHYK6ZZAwDASrF6mjNvrX0/yc/O6/ftDN9yBAC4SfHL9QAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnUw1eFXVXlX1zqo6u6rOqqr7V9Wtq+rEqjpn/Lv3NGsAAFgppt3idVSSD7TW7pLknknOSvL8JB9urR2S5MNjNwDATm9qwauq9kjyS0nelCSttR+11i5P8pgkx42jHZfksdOqAQBgJZlmi9cdkmxO8o9V9bmqemNV3TLJfq21i5Nk/HubhSauqmdU1caq2rh58+YplgkA0Mc0g9fqJPdO8trW2r2SfC9bcVmxtXZMa21da23dvvvuO60aAQC6mWbwuiDJBa21T4/d78wQxC6pqv2TZPx76RRrAABYMaYWvFpr30zyjaq689jrIUm+nOR9SY4Y+x2R5L3TqgEAYCVZPeX5r0/ylqq6eZJzk/xehrB3QlUdmeT8JE+Ycg0AACvCVINXa+2MJOsWGPSQaS4XAGAl8sv1AACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnQheAACdrJ51AQA3BUcffXQ2bdo06zLYBnP7bcOGDTOuhG21du3arF+/ftZlJBG8ALrYtGlTzjnzc7nd7tfOuhS20s1/PFwc+uHXN864ErbF+VetmnUJNyB4AXRyu92vzQvv/d1ZlwE3KS87fY9Zl3AD7vECAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhk9awLWGmOPvrobNq0adZlsA3m9tuGDRtmXAnbau3atVm/fv2sywCYGsFrnk2bNuWML52Va29x61mXwlba5UctSXLauZfMuBK2xarvXzbrEgCmTvBawLW3uHWuvsujZl0G3KTsdva/z7oEgKlzjxcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnSwavqtqvqt5UVf8xdh9aVUdOvzQAgJ3Lclq8jk3ywSQHjN3/leTZ0yoIAGBntZzgtU9r7YQkP0mS1to1Sa6dalUAADuh5fxy/feq6meTtCSpqvsluWI5M6+q85JcmSGoXdNaW1dVt07y9iQHJzkvyW+11r6z1ZUDAOxgltPi9cdJ3pfkjlX18STHJ9ma/2L7y621w1pr68bu5yf5cGvtkCQfHrsBAHZ6S7Z4tdZOr6oHJblzkkryldbaj2/EMh+T5PDx+XFJTk7yvBsxPwCAHcKSwauqfmNerztV1RVJvthau3SJyVuS/6yqluT1rbVjkuzXWrs4SVprF1fVbRZZ7jOSPCNJbne72y1VJgDAirece7yOTHL/JCeN3Ycn+VSGAPaXrbU3b2HaB7TWLhrD1YlVdfZyCxtD2jFJsm7durbc6QAAVqrl3OP1kyR3ba09vrX2+CSHJvlhkp/PEpcIW2sXjX8vTfKeJPdNcklV7Z8k49+lWs0AAHYKywleB7fWLpnovjTJnVprlyVZ9F6vqrplVd1q7nmShyf5UoYb9Y8YRzsiyXu3pXAAgB3Nci41nlpV70/yjrH78Uk+Ooapy7cw3X5J3lNVc8v559baB6rqs0lOGH/9/vwkT9jm6gEAdiDLCV7PSvIbSX5x7P5Mkv1ba99L8suLTdRaOzfJPRfo/+0kD9n6UgEAdmxLXmpsrbUkX81wWfFxGULTWVOuCwBgp7Noi1dV3SnJk5I8Ocm3M/zafLXWFm3lAgBgcVu61Hh2klOTPLq1tilJquo5XaoCANgJbelS4+OTfDPJSVX1hqp6SIZfrgcAYBssGrxaa+9prT0xyV0y/Fuf5yTZr6peW1UP71QfAMBOYzk313+vtfaW1tqvJTkwyRnxj60BALbacn5A9Tqttctaa69vrT14WgUBAOystip4AQCw7QQvAIBOBC8AgE4ELwCATgQvAIBOBC8AgE4ELwCATgQvAIBOBC8AgE4ELwCATgQvAIBOBC8AgE4ELwCATgQvAIBOVs+6AICbggsvvDDfu3JVXnb6HrMuBW5Svn7lqtzywgtnXcZ1tHgBAHSixQuggzVr1uSH11ycF977u7MuBW5SXnb6HvmZNWtmXcZ1tHgBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdDL14FVVq6rqc1X1/rH79lX16ao6p6reXlU3n3YNAAArQY8Wrw1JzprofmWSV7fWDknynSRHdqgBAGDmphq8qurAJL+a5I1jdyV5cJJ3jqMcl+Sx06wBAGClmHaL198l+dMkPxm7fzbJ5a21a8buC5KsWWjCqnpGVW2sqo2bN2+ecpkAANM3teBVVb+W5NLW2mmTvRcYtS00fWvtmNbautbaun333XcqNQIA9LR6ivN+QJJfr6pHJdk1yR4ZWsD2qqrVY6vXgUkummINAAArxtRavFprL2itHdhaOzjJk5J8pLX2lCQnJfnNcbQjkrx3WjUAAKwks/gdr+cl+eOq2pThnq83zaAGAIDupnmp8TqttZOTnDw+PzfJfXssFwBgJekSvHYkF154YVZ9/4rsdva/z7oUuElZ9f1v58ILr1l6RIAdmH8ZBADQiRavedasWZNv/nB1rr7Lo2ZdCtyk7Hb2v2fNmv1mXQbAVGnxAgDoRPACAOhE8AIA6ETwAgDoxM31AJ2cf9WqvOz0PWZdBlvpku8PbRT73eInM66EbXH+VatyyKyLmCB4AXSwdu3aWU+8vvYAAA5zSURBVJfANvrRpk1Jkp85yD7cER2SlfX6E7wAOli/fv2sS2AbbdiwIUly1FFHzbgSdgbu8QIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhE8AIA6ETwAgDoRPACAOhkasGrqnatqs9U1eer6syq+oux/+2r6tNVdU5Vvb2qbj6tGgAAVpJptnj9MMmDW2v3THJYkkdU1f2SvDLJq1trhyT5TpIjp1gDAMCKMbXg1QZXjZ03Gx8tyYOTvHPsf1ySx06rBgCAlWSq93hV1aqqOiPJpUlOTPLVJJe31q4ZR7kgyZpFpn1GVW2sqo2bN2+eZpkAAF1MNXi11q5trR2W5MAk901y14VGW2TaY1pr61pr6/bdd99plgkA0EWXbzW21i5PcnKS+yXZq6pWj4MOTHJRjxoAAGZtmt9q3Leq9hqf75bkoUnOSnJSkt8cRzsiyXunVQMAwEqyeulRttn+SY6rqlUZAt4JrbX3V9WXk7ytql6a5HNJ3jTFGgAAVoypBa/W2heS3GuB/udmuN8LAOAmxS/XAwB0IngBAHQieAEAdCJ4AQB0IngBAHQieAEAdCJ4AQB0Ms0fUN1hrfr+Zdnt7H+fdRlspV1+8N0kyU923WPGlbAtVn3/siT7zboMgKkSvOZZu3btrEtgG23adGWSZO0dvHnvmPbz+gN2eoLXPOvXr591CWyjDRs2JEmOOuqoGVcCAAtzjxcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCdTC15VdduqOqmqzqqqM6tqw9j/1lV1YlWdM/7de1o1AACsJNNs8bomyZ+01u6a5H5JnlVVhyZ5fpIPt9YOSfLhsRsAYKc3teDVWru4tXb6+PzKJGclWZPkMUmOG0c7Lsljp1UDAMBK0uUer6o6OMm9knw6yX6ttYuTIZwluc0i0zyjqjZW1cbNmzf3KBMAYKqmHryqavck70ry7Nbad5c7XWvtmNbautbaun333Xd6BQIAdDLV4FVVN8sQut7SWnv32PuSqtp/HL5/kkunWQMAwEoxzW81VpI3JTmrtfa3E4Pel+SI8fkRSd47rRoAAFaS1VOc9wOS/G6SL1bVGWO/FyZ5RZITqurIJOcnecIUawAAWDGmFrxaax9LUosMfsi0lgsAsFL55XoAgE4ELwCATgQvAIBOBC8AgE4ELwCATgQvAIBOBC8AgE4ELwCATgQvAIBOBC8AgE4ELwCATgQvAIBOBC8AgE4ELwCATgQvAIBOBC8AgE4ELwCATgQvAIBOBC8AgE4ELwCATgQvAIBOBC8AgE4ELwCATgQvAIBOBC8AgE4ELwCATgQvAIBOBC8AgE6qtTbrGpa0bt26tnHjxlmXsVM4+uijs2nTplmXMRVz67V27doZVzI9a9euzfr162ddBvwU55Ydm3PL9lVVp7XW1i00bHXvYmBadtttt1mXAOyEnFvYnrR4AQBsR1tq8XKPFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAnghcAQCeCFwBAJ4IXAEAn1VqbdQ1LqqrNSb4+6zrYIeyT5FuzLgLY6Ti3sDUOaq3tu9CAHSJ4wXJV1cbW2rpZ1wHsXJxb2F5cagQA6ETwAgDoRPBiZ3PMrAsAdkrOLWwX7vECAOhEixcAQCeCFwBAJ4IXO42qekRVfaWqNlXV82ddD7Djq6p/qKpLq+pLs66FnYPgxU6hqlYl+b9JHpnk0CRPrqpDZ1sVsBM4NskjZl0EOw/Bi53FfZNsaq2d21r7UZK3JXnMjGsCdnCttY8muWzWdbDzELzYWaxJ8o2J7gvGfgCwYghe7CxqgX5+KwWAFUXwYmdxQZLbTnQfmOSiGdUCAAsSvNhZfDbJIVV1+6q6eZInJXnfjGsCgBsQvNgptNauSfI/k3wwyVlJTmitnTnbqoAdXVW9Ncknk9y5qi6oqiNnXRM7Nv8yCACgEy1eAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXtBJVV015fm/p6rOqKpNVXXF+PyMqvqFqjqvqvaZ5vK3VVUdXlVt8mv6VXWvsd9zt/OyfmViu1xVVV8Znx9fVU+rqr/fnssbl7nd1m/uGKqqA6rqndupvmOr6je3x7yApQlesJNorT2utXZYkj9Icmpr7bDx8YlZ17YMX0zyxInuJyX5/NbMoKpWLzVOa+2Dc9slycYkTxm7n7pV1W69G71+k1prF7XWhCXYAQleMENVtW9VvauqPjs+HjD2f9BEy8znqupWVbV/VX107PelqnrgVi5ufVWdXlVfrKq7jMu5b1V9YlzGJ6rqzmP/p1XVu6vqA1V1TlW9aoHaH1lVJ0x0H15V/1pVq8ZWlC+Ny3rOMmo7P8muVbVfVVWSRyT5j4l5H1ZVn6qqL4wte3uP/U+uqpdV1SlJNiy2PbfCAQutc1W9tqo2VtWZVfUXE/3Pq6q/mL9dt2H97jgu97SqOnVi/9y+qj45rstfTYx/cFV9aeL5qWMNp1fVL4z9Dx+3zzur6uyqesu47EUttj5VtXtV/ePY7wtV9fix/5PHfl+qqldOzOeqqnrluD4fGo+zk6vq3Kr69XGcVVX11+O6faGq/nB5uwh2bIIXzNZRSV7dWrtPkscneePY/7lJnjW2zDwwydVJfjvJB8d+90xyxlYu61uttXsnee04/yQ5O8kvtdbuleTPkrxsYvzDMrTS3D3JE6tq8n9hJsmJSe5XVbccu5+Y5O3jdGtaa3drrd09yT8us753JnlCkl9IcnqSH04MOz7J81pr98jQevTnE8P2aq09qLX2N1l8ey7XYuv8otbauiT3SPKgqrrHxDQLbdetXb9jkqxvrf23cR6vGfsfleS14/p8c5H5XprkYWMNT0zy/00Mu1eSZyc5NMkdkiwniC60Pv8ryRWttbuP++AjVXVAklcmeXCG7XafqnrsOP4tk5w8rs+VSV6a5GFJHpfkL8dxjhzneZ8k90ny9Kq6/TLqgx3akk3zwFQ9NMmhEw0Re1TVrZJ8PMnfVtVbkry7tXZBVX02yT9U1c2S/EtrbWuD17vHv6cl+Y3x+Z5JjquqQ5K0JDebGP/DrbUrkqSqvpzkoCTfmBvYWrumqj6Q5NE13G/0q0n+NMN55Q5VdXSSf0vyn8us74QMwe0uSd6aIaCkqvbMEK5OGcc7Lsk7JqZ7+8TzBbdna+3KZdaw2Dr/VlU9Y1y3/TMEmS+M0yy0Xbdm/XYfn79jou6fGf8+IEOATJI3Zwg6890syd9X1WFJrk1yp4lhn2mtXTAu54wkByf52BZqXGx9Hprh8miSpLX2nar6pQzhavM4/7ck+aUk/5LkR0k+MI7+xSQ/bK39uKq+ONaQJA9Pco+6/v6yPZMckuRrS9QHOzTBC2ZrlyT3b61dPa//K6rq35I8KsmnquqhrbWPjm92v5rkzVX1162147diWXMtLNfm+tf+XyU5qbX2uKo6OMnJC4w/f5pJb0/yrCSXJfnsXMCpqnsm+ZVx2G8l+f2limutfbOqfpyhZWRDxmCyDN+beL7Y9lyun1rnsRXmuUnuMwaOY5PsusA0i22jJFtcv12SXD62ZC446RI1PyfJJRlaQXdJ8oMtrc8S85qcZnL8WqCOLV22/HG7/v/R/WRunq21n9T19+JVhla+Dy6jJthpuNQIs/WfGf65d5LhXqbx7x1ba19srb0yw03gd6mqg5Jc2lp7Q5I3Jbn3dlj+nkkuHJ8/bRumP3ms4+kZW55q+PbkLq21d2W4RLU1df5ZhkuK1871GFugvlPX39P2u0lOWWjiLLI9b6Q9MoS7K6pqvySPvBHzWmj9vpvka1X1hCSpwT3HwR/P9S1NT1lknnsmubi19pMM22bVjahvMfO3695JPp3hsus+VbUqyZOz+H5ZyAeT/PexBTdVdaeJy9aw0xK8oJ9bVNUFE48/TvJHSdaNNxd/Ockzx3GfPd6w/PkM93f9R5LDk5xRVZ/LcPnpqO1Q06uSvLyqPp5teMMeA8T7M4SR94+91yQ5eby0dWySFyRJVT2zqp650Hwm5veJ1tq/LDDoiCR/XVVfyHA/0V8uME6y+PbcZq21zyf5XJIzk/xDhjC0rfNabP2ekuTIcX+fmeQxY/8NSZ41Xmbec5HZvibJEVX1qQyXGb+3yHg3xkuT7D1xTP5ya+3iDPv2pAzf0Dy9tfberZjnG5N8Ocnp4xcFXh9XYbgJqOtbgwEAmCYtXgAAnQheAACdCF4AAJ0IXgAAnQheAACdCF4AAJ0IXgAAnfz/a+bEwIncQU0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DISTRIBUTION OF AGE \n",
    "fig = plt.figure(figsize=(10,10)) \n",
    "sns.boxplot(x=\"INCTOT\", y=\"AGE\", data=df)\n",
    "plt.title('Distribution of Age Between Those Who Made More or Less Than Median Income')\n",
    "plt.ylabel('Age')\n",
    "plt.xlabel('Less Than vs. More Than Median Income')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UHRSWORK1 VS INCTOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVNykYR4Rj4uIqyLiexFxQ0S8r5XvERFXRsTNEfG5iNiilT+2bS9v++f2nOuEVv6jiDiop3xhK1seEUuH/zIlSdp4DTIz/w2wf2Y+HXgGsDAi9gNOAk7OzHnAvcBxrf5xwL2Z+VTg5FaPiNgLOBJ4GrAQ+FhEbBYRmwGnAgcDewFHtbqSJGkAk4Z5dla3zc3bI4H9gfNa+VnAYe35oW2btv+AiIhWfk5m/iYzbwWWA89pj+WZeUtmPgSc0+pKkqQBDPSZeZtB/xtwN3Ax8O/AfZk52qqsAOa053OAOwDa/vuBJ/WWjztmonJJkjSAWYNUyszfAc+IiO2AfwX27Fet/RkT7JuovN8biuxTRkQsBhYDzJ49m5GRkXU3fB1Wr169QcdrjUp9uWSf0ckrTZPZW1KmH2e6SmNyprMvh+eR7MuBwnxMZt4XESPAfsB2ETGrzb53AVa2aiuAXYEVETEL2BZY1VM+pveYicrHX/804DSA+fPn54IFC6bS/IcZGRlhQ47XGpX68pilF0x3Eya0ZJ9RjijSjzNdpTE509mXw/NI9uUgq9l3bDNyImJL4CXATcClwCtbtUXA+e35srZN2//1zMxWfmRb7b4HMA+4CrgamNdWx29Bt0hu2TBenCRJm4JBZuY7AWe1VeePAc7NzC9FxI3AORHxQeA64PRW/3TgUxGxnG5GfiRAZt4QEecCNwKjwPHt9j0R8SbgImAz4IzMvGFor1CSpI3cpGGemd8Hntmn/Ba6lejjy38NHD7BuU4ETuxTfiFw4QDtlSRJ4/gNcJIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JU3KRhHhG7RsSlEXFTRNwQEW9p5U+MiIsj4ub25/atPCLilIhYHhHfj4hn9ZxrUat/c0Qs6in/k4j4QTvmlIiIR+LFSpK0MRpkZj4KLMnMPYH9gOMjYi9gKXBJZs4DLmnbAAcD89pjMfBx6MIfeA+wL/Ac4D1jbwBancU9xy3c8JcmSdKmYdIwz8w7M/O77fkDwE3AHOBQ4KxW7SzgsPb8UODs7FwBbBcROwEHARdn5qrMvBe4GFjY9j0hM7+TmQmc3XMuSZI0iSl9Zh4Rc4FnAlcCszPzTugCH3hyqzYHuKPnsBWtbF3lK/qUS5KkAcwatGJEbAN8HnhrZv5iHR9r99uR61Herw2L6W7HM3v2bEZGRiZp9cRWr169QcdrjUp9uWSf0eluwoRmb0mZfpzpKo3Jmc6+HJ5Hsi8HCvOI2JwuyP8lM7/Qiu+KiJ0y8852q/zuVr4C2LXn8F2Ala18wbjykVa+S5/6a8nM04DTAObPn58LFizoV20gIyMjbMjxWqNSXx6z9ILpbsKEluwzyhFF+nGmqzQmZzr7cngeyb4cZDV7AKcDN2XmR3p2LQPGVqQvAs7vKT+6rWrfD7i/3Ya/CDgwIrZvC98OBC5q+x6IiP3atY7uOZckSZrEIDPz5wGvBX4QEf/Wyt4BfBg4NyKOA24HDm/7LgReCiwHfgkcC5CZqyLiA8DVrd77M3NVe/7nwJnAlsCX20OSJA1g0jDPzG/S/3NtgAP61E/g+AnOdQZwRp/ya4C9J2uLJElam98AJ0lScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScYa5JEnFGeaSJBVnmEuSVJxhLklScZOGeUScERF3R8T1PWVPjIiLI+Lm9uf2rTwi4pSIWB4R34+IZ/Ucs6jVvzkiFvWU/0lE/KAdc0pExLBfpCRJG7NBZuZnAgvHlS0FLsnMecAlbRvgYGBeeywGPg5d+APvAfYFngO8Z+wNQKuzuOe48deSJEnrMGmYZ+ZlwKpxxYcCZ7XnZwGH9ZSfnZ0rgO0iYifgIODizFyVmfcCFwML274nZOZ3MjOBs3vOJUmSBrC+n5nPzsw7AdqfT27lc4A7euqtaGXrKl/Rp1ySJA1o1pDP1+/z7lyP8v4nj1hMd0ue2bNnMzIysh5N7KxevXqDjtcalfpyyT6j092ECc3ekjL9ONNVGpMznX05PI9kX65vmN8VETtl5p3tVvndrXwFsGtPvV2Ala18wbjykVa+S5/6fWXmacBpAPPnz88FCxZMVHVSIyMjbMjxWqNSXx6z9ILpbsKEluwzyhFF+nGmqzQmZzr7cngeyb5c39vsy4CxFemLgPN7yo9uq9r3A+5vt+EvAg6MiO3bwrcDgYvavgciYr+2iv3onnNJkqQBTDozj4jP0s2qd4iIFXSr0j8MnBsRxwG3A4e36hcCLwWWA78EjgXIzFUR8QHg6lbv/Zk5tqjuz+lWzG8JfLk9JEnSgCYN88w8aoJdB/Spm8DxE5znDOCMPuXXAHtP1g5JktSf3wAnSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFWeYS5JUnGEuSVJxhrkkScUZ5pIkFTdruhsgqZa5Sy+Y7ias020fftl0N0F61DkzlySpOMNckqTiDHNJkoozzCVJKs4wlySpOMNckqTiDHNJkoozzCVJKs4wlySpOL8BTkMz078ZTJI2Vs7MJUkqzjCXJKk4b7NLM4gfVUhaH87MJUkqzpl5ETN5xrZkn1GOmcHtk6SNnTNzSZKKM8wlSSrOMJckqTjDXJKk4gxzSZKKM8wlSSrOMJckqTjDXJKk4gxzSZKKM8wlSSrOMJckqTjDXJKk4gxzSZKKM8wlSSrOMJckqTj/P3NJG5W5Sy9gyT6jHLP0guluyoRu+/DLprsJ2sg4M5ckqTjDXJKk4gxzSZKKM8wlSSrOMJckqThXszdzZ/DKV0mS1sWZuSRJxRnmkiQVZ5hLklScYS5JUnGGuSRJxRnmkiQVZ5hLklScYS5JUnF+aYwkPcoqfEmV/01rLTNmZh4RCyPiRxGxPCKWTnd7JEmqYkbMzCNiM+BU4E+BFcDVEbEsM2+c3pZJ0qZp7O7Bkn1GOWYG3knwzsHDzZSZ+XOA5Zl5S2Y+BJwDHDrNbZIkqYQZMTMH5gB39GyvAPadprZIkmY41x083EwJ8+hTlmtVilgMLG6bqyPiRxtwzR2AezbgeDVvti+Hwn4cHvtyeOzL9RcnrVW0oX25+0Q7ZkqYrwB27dneBVg5vlJmngacNowLRsQ1mTl/GOfa1NmXw2E/Do99OTz25fA8kn05Uz4zvxqYFxF7RMQWwJHAsmlukyRJJcyImXlmjkbEm4CLgM2AMzLzhmluliRJJcyIMAfIzAuBCx/FSw7ldr0A+3JY7MfhsS+Hx74cnkesLyNzrXVmkiSpkJnymbkkSVpPm1yY+7Wx6y8ido2ISyPipoi4ISLe0sqfGBEXR8TN7c/tp7utVUTEZhFxXUR8qW3vERFXtr78XFsQqklExHYRcV5E/LCNz+c6LqcuIv6q/d2+PiI+GxGPc0wOLiLOiIi7I+L6nrK+4zA6p7Qs+n5EPGtDrr1JhXnP18YeDOwFHBURe01vq0oZBZZk5p7AfsDxrf+WApdk5jzgkratwbwFuKln+yTg5NaX9wLHTUur6vl74CuZ+cfA0+n61HE5BRExB3gzMD8z96ZbjHwkjsmpOBNYOK5sonF4MDCvPRYDH9+QC29SYY5fG7tBMvPOzPxue/4A3S/MOXR9eFardhZw2PS0sJaI2AV4GfCJth3A/sB5rYp9OYCIeALwQuB0gMx8KDPvw3G5PmYBW0bELGAr4E4ckwPLzMuAVeOKJxqHhwJnZ+cKYLuI2Gl9r72phXm/r42dM01tKS0i5gLPBK4EZmfmndAFPvDk6WtZKR8F3gb8R9t+EnBfZo62bcfnYJ4C/Az4ZPvI4hMRsTWOyynJzJ8A/wu4nS7E7weuxTG5oSYah0PNo00tzAf62litW0RsA3weeGtm/mK621NRRBwC3J2Z1/YW96nq+JzcLOBZwMcz85nAg3hLfcraZ7mHAnsAOwNb090KHs8xORxD/fu+qYX5QF8bq4lFxOZ0Qf4vmfmFVnzX2O2h9ufd09W+Qp4HvDwibqP7uGd/upn6du0WJzg+B7UCWJGZV7bt8+jC3XE5NS8Bbs3Mn2Xmb4EvAP8Fx+SGmmgcDjWPNrUw92tjN0D7TPd04KbM/EjPrmXAovZ8EXD+o922ajLzhMzcJTPn0o3Dr2fmq4FLgVe2avblADLzp8AdEfFHregA4EYcl1N1O7BfRGzV/q6P9aNjcsNMNA6XAUe3Ve37AfeP3Y5fH5vcl8ZExEvpZkBjXxt74jQ3qYyIeD5wOfAD1nzO+w66z83PBXaj+4VweGaOXwSiCUTEAuCvM/OQiHgK3Uz9icB1wGsy8zfT2b4KIuIZdAsJtwBuAY6lm6w4LqcgIt4HvIruX65cB7ye7nNcx+QAIuKzwAK6/x3tLuA9wP+lzzhsb5j+kW71+y+BYzPzmvW+9qYW5pIkbWw2tdvskiRtdAxzSZKKM8wlSSrOMJckqTjDXJKk4gxzSZKKM8wlSSrOMJckqbj/D83m7UHc2MeeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.UHRSWORK1.hist(figsize=(8,8))\n",
    "plt.title('Distribution of Hours Worked Per Week')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37.08469324958089, 42.879672897196265)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HRS1 = df.loc[df['INCTOT']==0]['UHRSWORK1'].mean()\n",
    "HRS2 = df.loc[df['INCTOT']==1]['UHRSWORK1'].mean()\n",
    "HRS1,HRS2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-68.02547821605323, pvalue=0.0)\n",
      "WE REJECT THE NULL HYPOTHESIS SINCE OUR P-VALUE IS LESS THAN 0.05. THOSE THAT DO MAKE MORE THAN 50K ARE SIGNIFICANTLY OLDER\n"
     ]
    }
   ],
   "source": [
    "# TWO SAMPLE T-TEST BETWEEN DEFAULT ON TARGET\n",
    "print(stats.ttest_ind(class_0['UHRSWORK1'], class_1['UHRSWORK1'], equal_var=False))\n",
    "print('WE REJECT THE NULL HYPOTHESIS SINCE OUR P-VALUE IS LESS THAN 0.05. THOSE THAT DO MAKE MORE THAN 50K ARE SIGNIFICANTLY OLDER')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col = 'INCTOT')\n",
    "g.map(plt.hist, 'UHRSWORK1');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RELATE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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": [
    "# BAR PLOT TO SHOW DISTRIBUTION\n",
    "fig,ax=plt.subplots(figsize=(20,10))\n",
    "ax= sns.countplot(x='RELATE',hue='INCTOT',data=df, color='darkred')\n",
    "ax.set_title('Difference of Relationship to Head of House Amongst Those Who Do/Do Not Make >50K',fontsize=15)\n",
    "ax.set_xlabel('Relationship to Head of House')\n",
    "ax.set_ylabel('Count Values')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ASECWT', 'RELATE', 'AGE', 'SEX', 'RACE', 'MARST', 'POPSTAT', 'VETSTAT',\n",
       "       'FAMREL', 'BPL', 'YRIMMIG', 'OCC2010', 'IND1990', 'CLASSWKR',\n",
       "       'UHRSWORK1', 'EDUC', 'EDUC99', 'DIFFANY', 'INCTOT'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATE DF FOR CHI SQUARE\n",
    "dfre = df.groupby('RELATE')['INCTOT'].value_counts()\n",
    "dfrei = dfre.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4976.72371316  2079.27628684]\n",
      " [  234.16557154    97.83442846]\n",
      " [24210.03988864 10114.96011136]\n",
      " [  333.61540764   139.38459236]\n",
      " [  595.99369864   249.00630136]\n",
      " [  671.4627232    280.5372768 ]\n",
      " [ 1028.3536244    429.6463756 ]\n",
      " [  514.88213019   215.11786981]\n",
      " [12725.3471135   5316.6528865 ]\n",
      " [ 1832.41612908   765.58387092]]\n",
      "probability=0.950, critical=16.919, stat=3558.372)\n",
      "Dependent (reject H0)\n",
      "signigicance=0.050, p=0.000,\n",
      "Dependent (reject H0)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import chi2\n",
    "# CHI SQUARE TEST\n",
    "stat, p, dof, expected = chi2_contingency(dfrei)\n",
    "print(expected)\n",
    "\n",
    "#interpret test-statistic\n",
    "prob = 0.95\n",
    "critical = chi2.ppf(prob, dof)\n",
    "print('probability=%.3f, critical=%.3f, stat=%.3f)' % (prob, critical, stat))\n",
    "if abs(stat) >= critical:\n",
    "    print('Dependent (reject H0)')\n",
    "else:\n",
    "    print('Independent (fail to reject H0)')\n",
    "#interpret p-value\n",
    "alpha= 1-prob\n",
    "print('signigicance=%.3f, p=%.3f,' % (alpha, p))\n",
    "if p <= alpha:\n",
    "    print('Dependent (reject H0)')\n",
    "else: \n",
    "    print('Independent (fail to reject)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>grandchild</th>\n",
       "      <th>head</th>\n",
       "      <th>other nonrelative</th>\n",
       "      <th>other relative</th>\n",
       "      <th>parent</th>\n",
       "      <th>roommate</th>\n",
       "      <th>sibling</th>\n",
       "      <th>spouse</th>\n",
       "      <th>unmarried partner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   grandchild  head  other nonrelative  other relative  parent  roommate  \\\n",
       "1           0     1                  0               0       0         0   \n",
       "2           0     0                  0               0       0         0   \n",
       "6           0     1                  0               0       0         0   \n",
       "7           0     0                  0               0       0         0   \n",
       "8           0     0                  0               0       0         0   \n",
       "\n",
       "   sibling  spouse  unmarried partner  \n",
       "1        0       0                  0  \n",
       "2        0       1                  0  \n",
       "6        0       0                  0  \n",
       "7        0       1                  0  \n",
       "8        0       0                  0  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DUMMIES FOR POST-HOC\n",
    "dummies = pd.get_dummies(df['RELATE'],drop_first=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INCTOT          0      1\n",
      "grandchild              \n",
      "0           46804  19675\n",
      "1             319     13 \n",
      "\n",
      "Chi2 value= 103.58494383158802\n",
      "p-value= 2.494658033774648e-24\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT      0      1\n",
      "head                \n",
      "0       24652   7834\n",
      "1       22471  11854 \n",
      "\n",
      "Chi2 value= 871.3084334112878\n",
      "p-value= 1.6948635985343722e-191\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                 0      1\n",
      "other nonrelative              \n",
      "0                  46714  19624\n",
      "1                    409     64 \n",
      "\n",
      "Chi2 value= 57.44745713936496\n",
      "p-value= 3.4712969320511024e-14\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT              0      1\n",
      "other relative              \n",
      "0               46353  19613\n",
      "1                 770     75 \n",
      "\n",
      "Chi2 value= 173.60531979647317\n",
      "p-value= 1.2071903630167e-39\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT      0      1\n",
      "parent              \n",
      "0       46336  19523\n",
      "1         787    165 \n",
      "\n",
      "Chi2 value= 67.84759984136457\n",
      "p-value= 1.7663346321791814e-16\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT        0      1\n",
      "roommate              \n",
      "0         45888  19465\n",
      "1          1235    223 \n",
      "\n",
      "Chi2 value= 143.36321747144177\n",
      "p-value= 4.89571161743127e-33\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT       0      1\n",
      "sibling              \n",
      "0        46447  19634\n",
      "1          676     54 \n",
      "\n",
      "Chi2 value= 171.908615030776\n",
      "p-value= 2.833473839655587e-39\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT      0      1\n",
      "spouse              \n",
      "0       35437  13332\n",
      "1       11686   6356 \n",
      "\n",
      "Chi2 value= 394.2612824920202\n",
      "p-value= 9.776955722241206e-88\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                 0      1\n",
      "unmarried partner              \n",
      "0                  45055  19158\n",
      "1                   2068    530 \n",
      "\n",
      "Chi2 value= 106.48610547962564\n",
      "p-value= 5.769560862382308e-25\n",
      "Degrees of freedom= 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#POST HOC TEST\n",
    "for series in dummies:\n",
    "    nl = \"\\n\"\n",
    "    \n",
    "    crosstab = pd.crosstab(dummies[f\"{series}\"], df['INCTOT'])\n",
    "    print(crosstab, nl)\n",
    "    chi2, p, dof, expected = stats.chi2_contingency(crosstab)\n",
    "    print(f\"Chi2 value= {chi2}{nl}p-value= {p}{nl}Degrees of freedom= {dof}{nl}\")\n",
    "    \n",
    "# HEAD OF HOUSE MAKES MORE THAN MEDIAN INCOME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RACE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BAR PLOT TO SHOW DISTRIBUTION\n",
    "fig,ax=plt.subplots(figsize=(15,8))\n",
    "ax= sns.countplot(x='RACE',hue='INCTOT',data=df, color='lightcoral')\n",
    "ax.set_title('Difference of Race Amongst Those Who Do/Do Not Make >50K',fontdict={'fontsize':'x-large'})\n",
    "ax.set_xlabel('Race',fontdict={'fontsize':'x-large'})\n",
    "ax.set_ylabel('Count Values',fontdict={'fontsize':'x-large'})\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "total = float(len(df) )\n",
    "\n",
    "ax = sns.countplot(x=\"BPL\",hue='INCTOT', data=df,palette='Blues')\n",
    "# for p in ax.patches:\n",
    "#     height = p.get_height()\n",
    "#     ax.text(p.get_x()+p.get_width()/2.,\n",
    "#             height + 3,\n",
    "#             '{:1.2f}'.format((height/total)*100),\n",
    "#             ha=\"center\") \n",
    "ax.set_title('Difference in Birthplace Amongst Those Who Do/Do Not Make >50K', fontdict={'fontsize':'x-large'})\n",
    "ax.set_xlabel('Race', fontdict={'fontsize':'x-large'})\n",
    "ax.set_ylabel('Count Value', fontdict={'fontsize':'x-large'})\n",
    "ax.set_xticklabels(\n",
    "ax.get_xticklabels(), \n",
    "rotation=60, \n",
    "horizontalalignment='right',\n",
    "fontweight='light',\n",
    "fontsize='x-large');    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATE DF FOR CHI SQUARE\n",
    "dfb = df.groupby('BPL')['INCTOT'].value_counts()\n",
    "dfbi = dfb.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.95838545e+02 2.07161455e+02]\n",
      " [1.88954689e+03 7.89453114e+02]\n",
      " [5.57201209e+01 2.32798791e+01]\n",
      " [8.54845400e+02 3.57154600e+02]\n",
      " [3.92791587e+03 1.64108413e+03]\n",
      " [6.74283995e+02 2.81716005e+02]\n",
      " [3.92248492e+04 1.63881508e+04]]\n",
      "probability=0.950, critical=12.592, stat=969.044)\n",
      "Dependent (reject H0)\n",
      "signigicance=0.050, p=0.000,\n",
      "Dependent (reject H0)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import chi2\n",
    "# CHI SQUARE TEST\n",
    "stat, p, dof, expected = chi2_contingency(dfbi)\n",
    "print(expected)\n",
    "\n",
    "#interpret test-statistic\n",
    "prob = 0.95\n",
    "critical = chi2.ppf(prob, dof)\n",
    "print('probability=%.3f, critical=%.3f, stat=%.3f)' % (prob, critical, stat))\n",
    "if abs(stat) >= critical:\n",
    "    print('Dependent (reject H0)')\n",
    "else:\n",
    "    print('Independent (fail to reject H0)')\n",
    "#interpret p-value\n",
    "alpha= 1-prob\n",
    "print('signigicance=%.3f, p=%.3f,' % (alpha, p))\n",
    "if p <= alpha:\n",
    "    print('Dependent (reject H0)')\n",
    "else: \n",
    "    print('Independent (fail to reject)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asia</th>\n",
       "      <th>australia-new-zealand</th>\n",
       "      <th>europe</th>\n",
       "      <th>north-america</th>\n",
       "      <th>south-america</th>\n",
       "      <th>usa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   asia  australia-new-zealand  europe  north-america  south-america  usa\n",
       "1     0                      0       0              0              0    1\n",
       "2     0                      0       0              0              0    1\n",
       "6     0                      0       0              1              0    0\n",
       "7     0                      0       0              0              0    1\n",
       "8     0                      0       0              0              0    1"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DUMMIES FOR POST-HOC\n",
    "dummies = pd.get_dummies(df['BPL'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INCTOT      0      1\n",
      "asia                \n",
      "0       45574  18558\n",
      "1        1549   1130 \n",
      "\n",
      "Chi2 value= 216.3413279812063\n",
      "p-value= 5.6815008510915454e-49\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT                     0      1\n",
      "australia-new-zealand              \n",
      "0                      47064  19668\n",
      "1                         59     20 \n",
      "\n",
      "Chi2 value= 0.4711942227067003\n",
      "p-value= 0.49243828271592105\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT      0      1\n",
      "europe              \n",
      "0       46407  19192\n",
      "1         716    496 \n",
      "\n",
      "Chi2 value= 77.38182902272275\n",
      "p-value= 1.4090560023290864e-18\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT             0      1\n",
      "north-america              \n",
      "0              42332  18910\n",
      "1               4791    778 \n",
      "\n",
      "Chi2 value= 701.2707609456801\n",
      "p-value= 1.58259282422192e-154\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT             0      1\n",
      "south-america              \n",
      "0              46413  19442\n",
      "1                710    246 \n",
      "\n",
      "Chi2 value= 6.332029189050567\n",
      "p-value= 0.011857656936742678\n",
      "Degrees of freedom= 1\n",
      "\n",
      "INCTOT      0      1\n",
      "usa                 \n",
      "0        8367   2831\n",
      "1       38756  16857 \n",
      "\n",
      "Chi2 value= 113.2223431528486\n",
      "p-value= 1.928856935328266e-26\n",
      "Degrees of freedom= 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#POST HOC TEST\n",
    "for series in dummies:\n",
    "    nl = \"\\n\"\n",
    "    \n",
    "    crosstab = pd.crosstab(dummies[f\"{series}\"], df['INCTOT'])\n",
    "    print(crosstab, nl)\n",
    "    chi2, p, dof, expected = stats.chi2_contingency(crosstab)\n",
    "    print(f\"Chi2 value= {chi2}{nl}p-value= {p}{nl}Degrees of freedom= {dof}{nl}\")\n",
    "    \n",
    "# THOSE BORN IN USA MAKE MORE THAN MEDIAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ASECWT VS INCTOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "INCTOT\n",
       "0    1870.479512\n",
       "1    1876.623954\n",
       "Name: ASECWT, dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('INCTOT').ASECWT.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1870.4795117034148, 1876.6239541852908)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asecwt1 = df.loc[df['INCTOT']==0]['ASECWT'].mean()\n",
    "asecwt2 = df.loc[df['INCTOT']==1]['ASECWT'].mean()\n",
    "asecwt1,asecwt2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-0.6109282326902186, pvalue=0.5412508139700223)\n",
      "WE FAIL TO REJECT THE NULL HYPOTHESIS SINCE OUR P-VALUE IS MORE THAN 0.05\n"
     ]
    }
   ],
   "source": [
    "# TWO SAMPLE T-TEST BETWEEN DEFAULT ON TARGET\n",
    "print(stats.ttest_ind(class_0['ASECWT'], class_1['ASECWT'], equal_var=False))\n",
    "print('WE FAIL TO REJECT THE NULL HYPOTHESIS SINCE OUR P-VALUE IS MORE THAN 0.05')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    66811.000000\n",
       "mean      1872.290168\n",
       "std       1191.448545\n",
       "min         96.650000\n",
       "25%        866.920000\n",
       "50%       1795.690000\n",
       "75%       2484.010000\n",
       "max       9869.740000\n",
       "Name: ASECWT, dtype: float64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ASECWT.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SEX VS TARGET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATE DF FOR CHI SQUARE\n",
    "dfs = df.groupby('SEX')['INCTOT'].value_counts()\n",
    "dfsi = dfs.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[24327.8279924 10164.1720076]\n",
      " [22795.1720076  9523.8279924]]\n",
      "probability=0.950, critical=3.841, stat=2170.144)\n",
      "Dependent (reject H0)\n",
      "signigicance=0.050, p=0.000,\n",
      "Dependent (reject H0)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "from scipy.stats import chi2\n",
    "# CHI SQUARE TEST\n",
    "stat, p, dof, expected = chi2_contingency(dfsi)\n",
    "print(expected)\n",
    "\n",
    "#interpret test-statistic\n",
    "prob = 0.95\n",
    "critical = chi2.ppf(prob, dof)\n",
    "print('probability=%.3f, critical=%.3f, stat=%.3f)' % (prob, critical, stat))\n",
    "if abs(stat) >= critical:\n",
    "    print('Dependent (reject H0)')\n",
    "else:\n",
    "    print('Independent (fail to reject H0)')\n",
    "#interpret p-value\n",
    "alpha= 1-prob\n",
    "print('signigicance=%.3f, p=%.3f,' % (alpha, p))\n",
    "if p <= alpha:\n",
    "    print('Dependent (reject H0)')\n",
    "else: \n",
    "    print('Independent (fail to reject)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SEX\n",
       "1    0.374232\n",
       "2    0.209784\n",
       "Name: INCTOT, dtype: float64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('SEX').INCTOT.mean()\n",
    "# MALES MAKE MORE THAN MEDIAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "total = float(len(df) )\n",
    "\n",
    "ax = sns.countplot(x=\"SEX\", data=df)\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x()+p.get_width()/2.,\n",
    "            height + 3,\n",
    "            '{:1.2f}'.format((height/total)*100),\n",
    "            ha=\"center\") \n",
    "plt.title(\"Difference in Sex Between \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# BAR PLOT TO SHOW DISTRIBUTION\n",
    "plt.figure(figsize = (10,8))\n",
    "ax= sns.countplot(x='SEX',hue='INCTOT',data=df, color='darkred')\n",
    "ax.set_title('Difference of Sex Between Those Who Do/Do Not Make >Median Income',fontsize=15)\n",
    "ax.set_xlabel('Sex')\n",
    "ax.set_ylabel('Count Values')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SEX  INCTOT\n",
       "1    0         21083\n",
       "     1         12904\n",
       "2    0         24972\n",
       "     1          6780\n",
       "Name: INCTOT, dtype: int64"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('SEX').INCTOT.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BIN AGE COLUMN\n",
    "bins=[0,25,34,90]\n",
    "labels=[0,1,2]\n",
    "df['AGE_NEW'] = pd.cut(df['AGE'],bins=bins, labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "conditions = [\n",
    "    df['EDUC'] == \"Associate's degree, occupational/vocational program\",\n",
    "    df['EDUC'] == \"Master's degree\",\n",
    "    df['EDUC'] == 'Grades 7 or 8',\n",
    "    df['EDUC'] == 'High school diploma or equivalent',\n",
    "    df['EDUC'] == 'Grade 11',\n",
    "    df['EDUC'] == \"Bachelor's degree\",\n",
    "    df['EDUC'] == 'Some college but no degree',\n",
    "    df['EDUC'] == 'Doctorate degree',\n",
    "    df['EDUC'] == \"Associate's degree, academic program\",\n",
    "    df['EDUC'] == 'Grade 10',\n",
    "    df['EDUC'] == '12th grade, no diploma',\n",
    "    df['EDUC'] == 'Professional school degree',\n",
    "    df['EDUC'] == 'Grade 9',\n",
    "    df['EDUC'] == 'Grades 1, 2, 3, or 4',\n",
    "    df['EDUC'] == 'None or preschool',\n",
    "    df['EDUC'] == 'Grades 5 or 6'\n",
    "]\n",
    "\n",
    "choices = [\n",
    "    1,\n",
    "    2,\n",
    "    0,\n",
    "    1,\n",
    "    0,\n",
    "    2,\n",
    "    1,\n",
    "    3,\n",
    "    1,\n",
    "    0,\n",
    "    0,\n",
    "    3,\n",
    "    0,\n",
    "    0,\n",
    "    0,\n",
    "    0\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['EDUCATION'] = np.select(conditions, choices, df['EDUC'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAKE HOURS WORKED PER WEEK BINARY\n",
    "df['WORK_HRS'] = np.where(df['UHRSWORK1'] >36, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#EXPORT DF TO MODEL\n",
    "df.to_csv('Modeling_set.csv')"
   ]
  },
  {
   "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": "3.6.9"
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 },
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