Project 2 - US Census.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "solution": "hidden"
   },
   "source": [
    "![rmotr](https://user-images.githubusercontent.com/7065401/39119486-4718e386-46ec-11e8-9fc3-5250a49ef570.png)\n",
    "<hr style=\"margin-bottom: 40px;\">\n",
    "\n",
    "<img src=\"https://archive.ics.uci.edu/ml/assets/MLimages/Large2.jpg\"\n",
    "    style=\"width:200px; float: right; margin: 0 40px 40px 40px;\"></img>\n",
    "\n",
    "# Project assignment: US Census Analysis\n",
    "\n",
    "<i>\n",
    "> Dataset source: https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data <br>\n",
    "> Dataset description: https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.names\n",
    "</i>\n",
    "\n",
    "We will be working with an extraction of data done by Barry Becker from the 1994 Census database.\n",
    "\n",
    "**[Dataset information and columns](Dataset Info.ipynb)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# magic function that renders the figure in a notebook\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "sns.set(rc={'figure.figsize': (18, 6)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the Dataset\n",
    "\n",
    "The data is located in the `data/census-data.csv` CSV file. Take a look at the structure of it and the fields it has. You will notice that **it doesn't have a header**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0                 1       2          3   4                   5   \\\n",
       "0  39         State-gov   77516  Bachelors  13       Never-married   \n",
       "1  50  Self-emp-not-inc   83311  Bachelors  13  Married-civ-spouse   \n",
       "2  38           Private  215646    HS-grad   9            Divorced   \n",
       "3  53           Private  234721       11th   7  Married-civ-spouse   \n",
       "4  28           Private  338409  Bachelors  13  Married-civ-spouse   \n",
       "\n",
       "                  6              7      8       9     10  11  12  \\\n",
       "0       Adm-clerical  Not-in-family  White    Male  2174   0  40   \n",
       "1    Exec-managerial        Husband  White    Male     0   0  13   \n",
       "2  Handlers-cleaners  Not-in-family  White    Male     0   0  40   \n",
       "3  Handlers-cleaners        Husband  Black    Male     0   0  40   \n",
       "4     Prof-specialty           Wife  Black  Female     0   0  40   \n",
       "\n",
       "              13     14  \n",
       "0  United-States  <=50K  \n",
       "1  United-States  <=50K  \n",
       "2  United-States  <=50K  \n",
       "3  United-States  <=50K  \n",
       "4           Cuba  <=50K  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/census-data.csv', sep=', ', engine='python', header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We used a few special parameters to correctly parse the csv. As the header is missing, pandas is assigning numbers to the columns.\n",
    "\n",
    "**This is when the fun starts...**\n",
    "<br><br><br>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Rename the columns\n",
    "\n",
    "Use the names contained in `col_names` to properly name the columns (and replace the numbers). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_names = ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status',\n",
    "             'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss',\n",
    "             'hours-per-week', 'native-country', 'income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], dtype='int64')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename the columns\n",
    "df.columns # printing this confirms the numbers are the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = col_names # set the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  fnlwgt  education  education-num  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       marital-status         occupation   relationship   race     sex  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week native-country income  \n",
       "0          2174             0              40  United-States  <=50K  \n",
       "1             0             0              13  United-States  <=50K  \n",
       "2             0             0              40  United-States  <=50K  \n",
       "3             0             0              40  United-States  <=50K  \n",
       "4             0             0              40           Cuba  <=50K  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the df.columns command with the first few rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once everything works, it'll look something like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  fnlwgt  education  education-num  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       marital-status         occupation   relationship   race     sex  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week native-country income  \n",
       "0          2174             0              40  United-States  <=50K  \n",
       "1             0             0              13  United-States  <=50K  \n",
       "2             0             0              40  United-States  <=50K  \n",
       "3             0             0              40  United-States  <=50K  \n",
       "4             0             0              40           Cuba  <=50K  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Cleaning the data\n",
    "\n",
    "You'll notice that some columns have invalid values (`workclass`, `occupation`, and `native-country`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### a. What are those invalid values?\n",
    "\n",
    "Identify the invalid values of `workclass` (spoiler, it's a `?` question mark):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>32531</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>32533</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>32534</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>32535</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>32537</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  workclass  fnlwgt  education  education-num  marital-status  \\\n",
       "0      False      False   False      False          False           False   \n",
       "1      False      False   False      False          False           False   \n",
       "2      False      False   False      False          False           False   \n",
       "3      False      False   False      False          False           False   \n",
       "4      False      False   False      False          False           False   \n",
       "5      False      False   False      False          False           False   \n",
       "6      False      False   False      False          False           False   \n",
       "7      False      False   False      False          False           False   \n",
       "8      False      False   False      False          False           False   \n",
       "9      False      False   False      False          False           False   \n",
       "10     False      False   False      False          False           False   \n",
       "11     False      False   False      False          False           False   \n",
       "12     False      False   False      False          False           False   \n",
       "13     False      False   False      False          False           False   \n",
       "14     False      False   False      False          False           False   \n",
       "15     False      False   False      False          False           False   \n",
       "16     False      False   False      False          False           False   \n",
       "17     False      False   False      False          False           False   \n",
       "18     False      False   False      False          False           False   \n",
       "19     False      False   False      False          False           False   \n",
       "20     False      False   False      False          False           False   \n",
       "21     False      False   False      False          False           False   \n",
       "22     False      False   False      False          False           False   \n",
       "23     False      False   False      False          False           False   \n",
       "24     False      False   False      False          False           False   \n",
       "25     False      False   False      False          False           False   \n",
       "26     False      False   False      False          False           False   \n",
       "27     False      False   False      False          False           False   \n",
       "28     False      False   False      False          False           False   \n",
       "29     False      False   False      False          False           False   \n",
       "...      ...        ...     ...        ...            ...             ...   \n",
       "32531  False      False   False      False          False           False   \n",
       "32532  False      False   False      False          False           False   \n",
       "32533  False      False   False      False          False           False   \n",
       "32534  False      False   False      False          False           False   \n",
       "32535  False      False   False      False          False           False   \n",
       "32536  False      False   False      False          False           False   \n",
       "32537  False      False   False      False          False           False   \n",
       "32538  False      False   False      False          False           False   \n",
       "32539  False      False   False      False          False           False   \n",
       "32540  False      False   False      False          False           False   \n",
       "32541  False      False   False      False          False           False   \n",
       "32542  False      False   False      False          False           False   \n",
       "32543  False      False   False      False          False           False   \n",
       "32544  False      False   False      False          False           False   \n",
       "32545  False      False   False      False          False           False   \n",
       "32546  False      False   False      False          False           False   \n",
       "32547  False      False   False      False          False           False   \n",
       "32548  False      False   False      False          False           False   \n",
       "32549  False      False   False      False          False           False   \n",
       "32550  False      False   False      False          False           False   \n",
       "32551  False      False   False      False          False           False   \n",
       "32552  False      False   False      False          False           False   \n",
       "32553  False      False   False      False          False           False   \n",
       "32554  False      False   False      False          False           False   \n",
       "32555  False      False   False      False          False           False   \n",
       "32556  False      False   False      False          False           False   \n",
       "32557  False      False   False      False          False           False   \n",
       "32558  False      False   False      False          False           False   \n",
       "32559  False      False   False      False          False           False   \n",
       "32560  False      False   False      False          False           False   \n",
       "\n",
       "       occupation  relationship   race    sex  capital-gain  capital-loss  \\\n",
       "0           False         False  False  False         False         False   \n",
       "1           False         False  False  False         False         False   \n",
       "2           False         False  False  False         False         False   \n",
       "3           False         False  False  False         False         False   \n",
       "4           False         False  False  False         False         False   \n",
       "5           False         False  False  False         False         False   \n",
       "6           False         False  False  False         False         False   \n",
       "7           False         False  False  False         False         False   \n",
       "8           False         False  False  False         False         False   \n",
       "9           False         False  False  False         False         False   \n",
       "10          False         False  False  False         False         False   \n",
       "11          False         False  False  False         False         False   \n",
       "12          False         False  False  False         False         False   \n",
       "13          False         False  False  False         False         False   \n",
       "14          False         False  False  False         False         False   \n",
       "15          False         False  False  False         False         False   \n",
       "16          False         False  False  False         False         False   \n",
       "17          False         False  False  False         False         False   \n",
       "18          False         False  False  False         False         False   \n",
       "19          False         False  False  False         False         False   \n",
       "20          False         False  False  False         False         False   \n",
       "21          False         False  False  False         False         False   \n",
       "22          False         False  False  False         False         False   \n",
       "23          False         False  False  False         False         False   \n",
       "24          False         False  False  False         False         False   \n",
       "25          False         False  False  False         False         False   \n",
       "26          False         False  False  False         False         False   \n",
       "27          False         False  False  False         False         False   \n",
       "28          False         False  False  False         False         False   \n",
       "29          False         False  False  False         False         False   \n",
       "...           ...           ...    ...    ...           ...           ...   \n",
       "32531       False         False  False  False         False         False   \n",
       "32532       False         False  False  False         False         False   \n",
       "32533       False         False  False  False         False         False   \n",
       "32534       False         False  False  False         False         False   \n",
       "32535       False         False  False  False         False         False   \n",
       "32536       False         False  False  False         False         False   \n",
       "32537       False         False  False  False         False         False   \n",
       "32538       False         False  False  False         False         False   \n",
       "32539       False         False  False  False         False         False   \n",
       "32540       False         False  False  False         False         False   \n",
       "32541       False         False  False  False         False         False   \n",
       "32542       False         False  False  False         False         False   \n",
       "32543       False         False  False  False         False         False   \n",
       "32544       False         False  False  False         False         False   \n",
       "32545       False         False  False  False         False         False   \n",
       "32546       False         False  False  False         False         False   \n",
       "32547       False         False  False  False         False         False   \n",
       "32548       False         False  False  False         False         False   \n",
       "32549       False         False  False  False         False         False   \n",
       "32550       False         False  False  False         False         False   \n",
       "32551       False         False  False  False         False         False   \n",
       "32552       False         False  False  False         False         False   \n",
       "32553       False         False  False  False         False         False   \n",
       "32554       False         False  False  False         False         False   \n",
       "32555       False         False  False  False         False         False   \n",
       "32556       False         False  False  False         False         False   \n",
       "32557       False         False  False  False         False         False   \n",
       "32558       False         False  False  False         False         False   \n",
       "32559       False         False  False  False         False         False   \n",
       "32560       False         False  False  False         False         False   \n",
       "\n",
       "       hours-per-week  native-country  income  \n",
       "0               False           False   False  \n",
       "1               False           False   False  \n",
       "2               False           False   False  \n",
       "3               False           False   False  \n",
       "4               False           False   False  \n",
       "5               False           False   False  \n",
       "6               False           False   False  \n",
       "7               False           False   False  \n",
       "8               False           False   False  \n",
       "9               False           False   False  \n",
       "10              False           False   False  \n",
       "11              False           False   False  \n",
       "12              False           False   False  \n",
       "13              False           False   False  \n",
       "14              False           False   False  \n",
       "15              False           False   False  \n",
       "16              False           False   False  \n",
       "17              False           False   False  \n",
       "18              False           False   False  \n",
       "19              False           False   False  \n",
       "20              False           False   False  \n",
       "21              False           False   False  \n",
       "22              False           False   False  \n",
       "23              False           False   False  \n",
       "24              False           False   False  \n",
       "25              False           False   False  \n",
       "26              False           False   False  \n",
       "27              False           False   False  \n",
       "28              False           False   False  \n",
       "29              False           False   False  \n",
       "...               ...             ...     ...  \n",
       "32531           False           False   False  \n",
       "32532           False           False   False  \n",
       "32533           False           False   False  \n",
       "32534           False           False   False  \n",
       "32535           False           False   False  \n",
       "32536           False           False   False  \n",
       "32537           False           False   False  \n",
       "32538           False           False   False  \n",
       "32539           False           False   False  \n",
       "32540           False           False   False  \n",
       "32541           False           False   False  \n",
       "32542           False           False   False  \n",
       "32543           False           False   False  \n",
       "32544           False           False   False  \n",
       "32545           False           False   False  \n",
       "32546           False           False   False  \n",
       "32547           False           False   False  \n",
       "32548           False           False   False  \n",
       "32549           False           False   False  \n",
       "32550           False           False   False  \n",
       "32551           False           False   False  \n",
       "32552           False           False   False  \n",
       "32553           False           False   False  \n",
       "32554           False           False   False  \n",
       "32555           False           False   False  \n",
       "32556           False           False   False  \n",
       "32557           False           False   False  \n",
       "32558           False           False   False  \n",
       "32559           False           False   False  \n",
       "32560           False           False   False  \n",
       "\n",
       "[32561 rows x 15 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ORIGINAL RESULTS ABOVE, test code below\n",
    "df.isna() # prints out the whole thing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private             22696\n",
       "Self-emp-not-inc     2541\n",
       "Local-gov            2093\n",
       "?                    1836\n",
       "State-gov            1298\n",
       "Self-emp-inc         1116\n",
       "Federal-gov           960\n",
       "Without-pay            14\n",
       "Never-worked            7\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['workclass'].value_counts()  # almost forgot this is a method\n",
    "# method reveals that there are a ton of question marks (?)\n",
    "# workclass, occupation, and native-country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prof-specialty       4140\n",
       "Craft-repair         4099\n",
       "Exec-managerial      4066\n",
       "Adm-clerical         3770\n",
       "Sales                3650\n",
       "Other-service        3295\n",
       "Machine-op-inspct    2002\n",
       "?                    1843\n",
       "Transport-moving     1597\n",
       "Handlers-cleaners    1370\n",
       "Farming-fishing       994\n",
       "Tech-support          928\n",
       "Protective-serv       649\n",
       "Priv-house-serv       149\n",
       "Armed-Forces            9\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['occupation'].value_counts()  # almost forgot this is a method\n",
    "# method reveals that there are a ton of question marks (?)\n",
    "# workclass, occupation, and native-country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United-States                 29170\n",
       "Mexico                          643\n",
       "?                               583\n",
       "Philippines                     198\n",
       "Germany                         137\n",
       "Canada                          121\n",
       "Puerto-Rico                     114\n",
       "El-Salvador                     106\n",
       "India                           100\n",
       "Cuba                             95\n",
       "England                          90\n",
       "Jamaica                          81\n",
       "South                            80\n",
       "China                            75\n",
       "Italy                            73\n",
       "Dominican-Republic               70\n",
       "Vietnam                          67\n",
       "Guatemala                        64\n",
       "Japan                            62\n",
       "Poland                           60\n",
       "Columbia                         59\n",
       "Taiwan                           51\n",
       "Haiti                            44\n",
       "Iran                             43\n",
       "Portugal                         37\n",
       "Nicaragua                        34\n",
       "Peru                             31\n",
       "France                           29\n",
       "Greece                           29\n",
       "Ecuador                          28\n",
       "Ireland                          24\n",
       "Hong                             20\n",
       "Trinadad&Tobago                  19\n",
       "Cambodia                         19\n",
       "Thailand                         18\n",
       "Laos                             18\n",
       "Yugoslavia                       16\n",
       "Outlying-US(Guam-USVI-etc)       14\n",
       "Honduras                         13\n",
       "Hungary                          13\n",
       "Scotland                         12\n",
       "Holand-Netherlands                1\n",
       "Name: native-country, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['native-country'].value_counts()  # almost forgot this is a method\n",
    "# method reveals that there are a ton of question marks (?)\n",
    "# workclass, occupation, and native-country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['workclass', 'occupation', 'native-country'].value_counts() # Can't do multiple columns!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>workclass</th>\n",
       "      <th>occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>Adm-clerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Exec-managerial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Private</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Private</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Private</td>\n",
       "      <td>Prof-specialty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Private</td>\n",
       "      <td>Exec-managerial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Private</td>\n",
       "      <td>Other-service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Exec-managerial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Private</td>\n",
       "      <td>Prof-specialty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Private</td>\n",
       "      <td>Exec-managerial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Private</td>\n",
       "      <td>Exec-managerial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>Prof-specialty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Private</td>\n",
       "      <td>Adm-clerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Private</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Private</td>\n",
       "      <td>Craft-repair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Private</td>\n",
       "      <td>Transport-moving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Farming-fishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Private</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Private</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Exec-managerial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Private</td>\n",
       "      <td>Prof-specialty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Private</td>\n",
       "      <td>Other-service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>Farming-fishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Private</td>\n",
       "      <td>Transport-moving</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Private</td>\n",
       "      <td>Tech-support</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Tech-support</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Private</td>\n",
       "      <td>Craft-repair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Private</td>\n",
       "      <td>Exec-managerial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Private</td>\n",
       "      <td>Craft-repair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32531</th>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32532</th>\n",
       "      <td>Private</td>\n",
       "      <td>Prof-specialty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32533</th>\n",
       "      <td>Private</td>\n",
       "      <td>Exec-managerial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32534</th>\n",
       "      <td>Private</td>\n",
       "      <td>Adm-clerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32535</th>\n",
       "      <td>Private</td>\n",
       "      <td>Protective-serv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32536</th>\n",
       "      <td>Private</td>\n",
       "      <td>Exec-managerial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32537</th>\n",
       "      <td>Private</td>\n",
       "      <td>Craft-repair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32538</th>\n",
       "      <td>Private</td>\n",
       "      <td>Prof-specialty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32539</th>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32540</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>Adm-clerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32541</th>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32542</th>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32543</th>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Prof-specialty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32544</th>\n",
       "      <td>Private</td>\n",
       "      <td>Other-service</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32545</th>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Adm-clerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32546</th>\n",
       "      <td>Private</td>\n",
       "      <td>Tech-support</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32547</th>\n",
       "      <td>Private</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32548</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Prof-specialty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32549</th>\n",
       "      <td>State-gov</td>\n",
       "      <td>Adm-clerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32550</th>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Craft-repair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32551</th>\n",
       "      <td>Private</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32552</th>\n",
       "      <td>Private</td>\n",
       "      <td>Sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32553</th>\n",
       "      <td>Private</td>\n",
       "      <td>Tech-support</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32554</th>\n",
       "      <td>Private</td>\n",
       "      <td>Exec-managerial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</th>\n",
       "      <td>Private</td>\n",
       "      <td>Protective-serv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>Private</td>\n",
       "      <td>Tech-support</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>Private</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>Private</td>\n",
       "      <td>Adm-clerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>Private</td>\n",
       "      <td>Adm-clerical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>Exec-managerial</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              workclass         occupation\n",
       "0             State-gov       Adm-clerical\n",
       "1      Self-emp-not-inc    Exec-managerial\n",
       "2               Private  Handlers-cleaners\n",
       "3               Private  Handlers-cleaners\n",
       "4               Private     Prof-specialty\n",
       "5               Private    Exec-managerial\n",
       "6               Private      Other-service\n",
       "7      Self-emp-not-inc    Exec-managerial\n",
       "8               Private     Prof-specialty\n",
       "9               Private    Exec-managerial\n",
       "10              Private    Exec-managerial\n",
       "11            State-gov     Prof-specialty\n",
       "12              Private       Adm-clerical\n",
       "13              Private              Sales\n",
       "14              Private       Craft-repair\n",
       "15              Private   Transport-moving\n",
       "16     Self-emp-not-inc    Farming-fishing\n",
       "17              Private  Machine-op-inspct\n",
       "18              Private              Sales\n",
       "19     Self-emp-not-inc    Exec-managerial\n",
       "20              Private     Prof-specialty\n",
       "21              Private      Other-service\n",
       "22          Federal-gov    Farming-fishing\n",
       "23              Private   Transport-moving\n",
       "24              Private       Tech-support\n",
       "25            Local-gov       Tech-support\n",
       "26              Private       Craft-repair\n",
       "27                    ?                  ?\n",
       "28              Private    Exec-managerial\n",
       "29              Private       Craft-repair\n",
       "...                 ...                ...\n",
       "32531                 ?                  ?\n",
       "32532           Private     Prof-specialty\n",
       "32533           Private    Exec-managerial\n",
       "32534           Private       Adm-clerical\n",
       "32535           Private    Protective-serv\n",
       "32536           Private    Exec-managerial\n",
       "32537           Private       Craft-repair\n",
       "32538           Private     Prof-specialty\n",
       "32539                 ?                  ?\n",
       "32540         State-gov       Adm-clerical\n",
       "32541                 ?                  ?\n",
       "32542                 ?                  ?\n",
       "32543         Local-gov     Prof-specialty\n",
       "32544           Private      Other-service\n",
       "32545         Local-gov       Adm-clerical\n",
       "32546           Private       Tech-support\n",
       "32547           Private  Machine-op-inspct\n",
       "32548  Self-emp-not-inc     Prof-specialty\n",
       "32549         State-gov       Adm-clerical\n",
       "32550  Self-emp-not-inc       Craft-repair\n",
       "32551           Private  Handlers-cleaners\n",
       "32552           Private              Sales\n",
       "32553           Private       Tech-support\n",
       "32554           Private    Exec-managerial\n",
       "32555           Private    Protective-serv\n",
       "32556           Private       Tech-support\n",
       "32557           Private  Machine-op-inspct\n",
       "32558           Private       Adm-clerical\n",
       "32559           Private       Adm-clerical\n",
       "32560      Self-emp-inc    Exec-managerial\n",
       "\n",
       "[32561 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['workclass','occupation']] # no value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>workclass</th>\n",
       "      <th>occupation</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32531</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32532</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32533</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32534</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32535</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32536</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32537</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32538</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32539</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32540</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32541</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32542</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32543</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32544</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32545</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32546</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32547</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32548</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32549</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32550</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32551</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32552</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32553</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32554</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       workclass  occupation  native-country\n",
       "0          False       False           False\n",
       "1          False       False           False\n",
       "2          False       False           False\n",
       "3          False       False           False\n",
       "4          False       False           False\n",
       "5          False       False           False\n",
       "6          False       False           False\n",
       "7          False       False           False\n",
       "8          False       False           False\n",
       "9          False       False           False\n",
       "10         False       False           False\n",
       "11         False       False           False\n",
       "12         False       False           False\n",
       "13         False       False           False\n",
       "14         False       False           False\n",
       "15         False       False           False\n",
       "16         False       False           False\n",
       "17         False       False           False\n",
       "18         False       False           False\n",
       "19         False       False           False\n",
       "20         False       False           False\n",
       "21         False       False           False\n",
       "22         False       False           False\n",
       "23         False       False           False\n",
       "24         False       False           False\n",
       "25         False       False           False\n",
       "26         False       False           False\n",
       "27         False       False           False\n",
       "28         False       False           False\n",
       "29         False       False           False\n",
       "...          ...         ...             ...\n",
       "32531      False       False           False\n",
       "32532      False       False           False\n",
       "32533      False       False           False\n",
       "32534      False       False           False\n",
       "32535      False       False           False\n",
       "32536      False       False           False\n",
       "32537      False       False           False\n",
       "32538      False       False           False\n",
       "32539      False       False           False\n",
       "32540      False       False           False\n",
       "32541      False       False           False\n",
       "32542      False       False           False\n",
       "32543      False       False           False\n",
       "32544      False       False           False\n",
       "32545      False       False           False\n",
       "32546      False       False           False\n",
       "32547      False       False           False\n",
       "32548      False       False           False\n",
       "32549      False       False           False\n",
       "32550      False       False           False\n",
       "32551      False       False           False\n",
       "32552      False       False           False\n",
       "32553      False       False           False\n",
       "32554      False       False           False\n",
       "32555      False       False           False\n",
       "32556      False       False           False\n",
       "32557      False       False           False\n",
       "32558      False       False           False\n",
       "32559      False       False           False\n",
       "32560      False       False           False\n",
       "\n",
       "[32561 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for NaN\n",
    "# pandas.isnull --> NaN in numeric arrays, None or NaN in object arrays, NaT in datetimelike\n",
    "\n",
    "df[['workclass','occupation','native-country']].isnull()\n",
    "# prints the whole set of data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "workclass         False\n",
       "occupation        False\n",
       "native-country    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['workclass','occupation','native-country']].isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution": "hidden"
   },
   "source": [
    "###### b. Removing those rows\n",
    "\n",
    "Your task now is to remove those invalid rows (all the ones containing `?`). There are different ways of doing this, so this is entirely up to you. This is the expected output of your dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace({\n",
    "    'workclass': { '?': np.nan },\n",
    "    'native-country': { '?': np.nan }\n",
    "}, inplace=True)\n",
    "\n",
    "# error with all 3?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30718 entries, 0 to 32560\n",
      "Data columns (total 17 columns):\n",
      "age               30718 non-null int64\n",
      "workclass         30718 non-null object\n",
      "fnlwgt            30718 non-null int64\n",
      "education         30718 non-null object\n",
      "education-num     30718 non-null int64\n",
      "marital-status    30718 non-null object\n",
      "occupation        30718 non-null object\n",
      "relationship      30718 non-null object\n",
      "race              30718 non-null object\n",
      "sex               30718 non-null object\n",
      "capital-gain      30718 non-null int64\n",
      "capital-loss      30718 non-null int64\n",
      "hours-per-week    30718 non-null int64\n",
      "native-country    30162 non-null object\n",
      "income            30718 non-null category\n",
      "<=50K             30718 non-null uint8\n",
      ">50K              30718 non-null uint8\n",
      "dtypes: category(1), int64(6), object(8), uint8(2)\n",
      "memory usage: 4.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace({\n",
    "    'occupation':{'?',np.nan}\n",
    "}, inplace=True)\n",
    "\n",
    "# error with all 3?\n",
    "# had to run this separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      "age               32561 non-null int64\n",
      "workclass         30725 non-null object\n",
      "fnlwgt            32561 non-null int64\n",
      "education         32561 non-null object\n",
      "education-num     32561 non-null int64\n",
      "marital-status    32561 non-null object\n",
      "occupation        32561 non-null object\n",
      "relationship      32561 non-null object\n",
      "race              32561 non-null object\n",
      "sex               32561 non-null object\n",
      "capital-gain      32561 non-null int64\n",
      "capital-loss      32561 non-null int64\n",
      "hours-per-week    32561 non-null int64\n",
      "native-country    31978 non-null object\n",
      "income            32561 non-null object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# My results are below\n",
    "df.info()\n",
    "\n",
    "# workclass and native country have less values with the '?'s removed, correct?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30169 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      "age               30169 non-null int64\n",
      "workclass         30169 non-null object\n",
      "fnlwgt            30169 non-null int64\n",
      "education         30169 non-null object\n",
      "education-num     30169 non-null int64\n",
      "marital-status    30169 non-null object\n",
      "occupation        30169 non-null object\n",
      "relationship      30169 non-null object\n",
      "race              30169 non-null object\n",
      "sex               30169 non-null object\n",
      "capital-gain      30169 non-null int64\n",
      "capital-loss      30169 non-null int64\n",
      "hours-per-week    30169 non-null int64\n",
      "native-country    30169 non-null object\n",
      "income            30169 non-null object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 4.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# Result:\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stack overflow\n",
    "# rechecking what's not null\n",
    "df.isnull().values.any() # nope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               False\n",
       "workclass          True\n",
       "fnlwgt            False\n",
       "education         False\n",
       "education-num     False\n",
       "marital-status    False\n",
       "occupation        False\n",
       "relationship      False\n",
       "race              False\n",
       "sex               False\n",
       "capital-gain      False\n",
       "capital-loss      False\n",
       "hours-per-week    False\n",
       "native-country     True\n",
       "income            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any() # shows workclass and native-country have some null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html\n",
    "# DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)\n",
    "\n",
    "# axis : {0 or ‘index’, 1 or ‘columns’}, default 0\n",
    "# 0, or ‘index’ : Drop rows which contain missing values.\n",
    "# 1, or ‘columns’ : Drop columns which contain missing value.\n",
    "\n",
    "# how : {‘any’, ‘all’}, default ‘any’\n",
    "# Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.\n",
    "# ‘any’ : If any NA values are present, drop that row or column.\n",
    "# ‘all’ : If all values are NA, drop that row or column.\n",
    "\n",
    "# inplace : bool, default False\n",
    "# If True, do operation inplace and return None.\n",
    "\n",
    "df.dropna(axis=0,how='any',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 30162 entries, 0 to 32560\n",
      "Data columns (total 17 columns):\n",
      "age               30162 non-null int64\n",
      "workclass         30162 non-null object\n",
      "fnlwgt            30162 non-null int64\n",
      "education         30162 non-null object\n",
      "education-num     30162 non-null int64\n",
      "marital-status    30162 non-null object\n",
      "occupation        30162 non-null object\n",
      "relationship      30162 non-null object\n",
      "race              30162 non-null object\n",
      "sex               30162 non-null object\n",
      "capital-gain      30162 non-null int64\n",
      "capital-loss      30162 non-null int64\n",
      "hours-per-week    30162 non-null int64\n",
      "native-country    30162 non-null object\n",
      "income            30162 non-null category\n",
      "<=50K             30162 non-null uint8\n",
      ">50K              30162 non-null uint8\n",
      "dtypes: category(1), int64(6), object(8), uint8(2)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check the values again\n",
    "df.info()\n",
    "# every set now has the same amount of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private             22286\n",
       "Self-emp-not-inc     2499\n",
       "Local-gov            2067\n",
       "State-gov            1279\n",
       "Self-emp-inc         1074\n",
       "Federal-gov           943\n",
       "Without-pay            14\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['workclass'].value_counts() # prove that I do not have any question mark values left in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prof-specialty       4038\n",
       "Craft-repair         4030\n",
       "Exec-managerial      3992\n",
       "Adm-clerical         3721\n",
       "Sales                3584\n",
       "Other-service        3212\n",
       "Machine-op-inspct    1966\n",
       "Transport-moving     1572\n",
       "Handlers-cleaners    1350\n",
       "Farming-fishing       989\n",
       "Tech-support          912\n",
       "Protective-serv       644\n",
       "Priv-house-serv       143\n",
       "Armed-Forces            9\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['occupation'].value_counts() # still question marks, why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180211</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>South</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293936</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200681</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>212759</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>304873</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>34095</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129305</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174662</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>252903</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38317</td>\n",
       "      <td>1st-4th</td>\n",
       "      <td>2</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135105</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170653</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Italy</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>187656</td>\n",
       "      <td>1st-4th</td>\n",
       "      <td>2</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24215</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119156</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>218956</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>Canada</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157443</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>3464</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>860348</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211601</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36135</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151989</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>?</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>372020</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>137300</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123147</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>1887</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107762</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>202521</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>258872</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>115745</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139883</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>271749</td>\n",
       "      <td>12th</td>\n",
       "      <td>8</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>594</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>401531</td>\n",
       "      <td>1st-4th</td>\n",
       "      <td>2</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32062</th>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375313</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32070</th>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97474</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32073</th>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192825</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32075</th>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147638</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Other</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Japan</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32080</th>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155697</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32083</th>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43909</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>?</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32088</th>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78374</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32093</th>\n",
       "      <td>62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>263374</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Canada</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32103</th>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330301</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Separated</td>\n",
       "      <td>?</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32189</th>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>204868</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32201</th>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113913</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32206</th>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96867</td>\n",
       "      <td>5th-6th</td>\n",
       "      <td>3</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32275</th>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99891</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32291</th>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120617</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32304</th>\n",
       "      <td>30</td>\n",
       "      <td>Never-worked</td>\n",
       "      <td>176673</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32310</th>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205939</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32314</th>\n",
       "      <td>18</td>\n",
       "      <td>Never-worked</td>\n",
       "      <td>153663</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32317</th>\n",
       "      <td>63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126540</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32335</th>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156608</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32343</th>\n",
       "      <td>66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93318</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32426</th>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>203992</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32477</th>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114648</td>\n",
       "      <td>12th</td>\n",
       "      <td>8</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>?</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32490</th>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>134152</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32494</th>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>403910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32525</th>\n",
       "      <td>81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120478</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>?</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32530</th>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320084</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32531</th>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33811</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32539</th>\n",
       "      <td>71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>287372</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32541</th>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>202822</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32542</th>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129912</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1843 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age     workclass  fnlwgt     education  education-num  \\\n",
       "27      54           NaN  180211  Some-college             10   \n",
       "61      32           NaN  293936       7th-8th              4   \n",
       "69      25           NaN  200681  Some-college             10   \n",
       "77      67           NaN  212759          10th              6   \n",
       "106     17           NaN  304873          10th              6   \n",
       "128     35           NaN  129305       HS-grad              9   \n",
       "149     43           NaN  174662  Some-college             10   \n",
       "154     52           NaN  252903       HS-grad              9   \n",
       "160     68           NaN   38317       1st-4th              2   \n",
       "187     53           NaN  135105     Bachelors             13   \n",
       "201     19           NaN  170653       HS-grad              9   \n",
       "221     64           NaN  187656       1st-4th              2   \n",
       "226     60           NaN   24215          10th              6   \n",
       "243     20           NaN  119156  Some-college             10   \n",
       "266     19           NaN  218956  Some-college             10   \n",
       "297     39           NaN  157443       Masters             14   \n",
       "312     19           NaN  860348  Some-college             10   \n",
       "326     23           NaN  211601     Assoc-voc             11   \n",
       "346     67           NaN   36135          11th              7   \n",
       "347     30           NaN  151989     Assoc-voc             11   \n",
       "354     59           NaN  372020     Bachelors             13   \n",
       "397     20           NaN  137300       HS-grad              9   \n",
       "408     28           NaN  123147  Some-college             10   \n",
       "430     80           NaN  107762       HS-grad              9   \n",
       "431     17           NaN  202521          11th              7   \n",
       "449     17           NaN  258872          11th              7   \n",
       "459     32           NaN  115745  Some-college             10   \n",
       "471     22           NaN  139883  Some-college             10   \n",
       "484     52           NaN  271749          12th              8   \n",
       "486     63           NaN  401531       1st-4th              2   \n",
       "...    ...           ...     ...           ...            ...   \n",
       "32062   26           NaN  375313  Some-college             10   \n",
       "32070   18           NaN   97474       HS-grad              9   \n",
       "32073   65           NaN  192825       7th-8th              4   \n",
       "32075   27           NaN  147638       Masters             14   \n",
       "32080   21           NaN  155697           9th              5   \n",
       "32083   51           NaN   43909       HS-grad              9   \n",
       "32088   21           NaN   78374       HS-grad              9   \n",
       "32093   62           NaN  263374     Assoc-voc             11   \n",
       "32103   34           NaN  330301       7th-8th              4   \n",
       "32189   19           NaN  204868       HS-grad              9   \n",
       "32201   49           NaN  113913       HS-grad              9   \n",
       "32206   72           NaN   96867       5th-6th              3   \n",
       "32275   20           NaN   99891  Some-college             10   \n",
       "32291   59           NaN  120617  Some-college             10   \n",
       "32304   30  Never-worked  176673       HS-grad              9   \n",
       "32310   21           NaN  205939  Some-college             10   \n",
       "32314   18  Never-worked  153663  Some-college             10   \n",
       "32317   63           NaN  126540  Some-college             10   \n",
       "32335   18           NaN  156608          11th              7   \n",
       "32343   66           NaN   93318       HS-grad              9   \n",
       "32426   20           NaN  203992       HS-grad              9   \n",
       "32477   49           NaN  114648          12th              8   \n",
       "32490   60           NaN  134152           9th              5   \n",
       "32494   82           NaN  403910       HS-grad              9   \n",
       "32525   81           NaN  120478     Assoc-voc             11   \n",
       "32530   35           NaN  320084     Bachelors             13   \n",
       "32531   30           NaN   33811     Bachelors             13   \n",
       "32539   71           NaN  287372     Doctorate             16   \n",
       "32541   41           NaN  202822       HS-grad              9   \n",
       "32542   72           NaN  129912       HS-grad              9   \n",
       "\n",
       "              marital-status occupation    relationship                race  \\\n",
       "27        Married-civ-spouse          ?         Husband  Asian-Pac-Islander   \n",
       "61     Married-spouse-absent          ?   Not-in-family               White   \n",
       "69             Never-married          ?       Own-child               White   \n",
       "77        Married-civ-spouse          ?         Husband               White   \n",
       "106            Never-married          ?       Own-child               White   \n",
       "128       Married-civ-spouse          ?         Husband               White   \n",
       "149                 Divorced          ?   Not-in-family               White   \n",
       "154                 Divorced          ?   Not-in-family               White   \n",
       "160                 Divorced          ?   Not-in-family               White   \n",
       "187                 Divorced          ?   Not-in-family               White   \n",
       "201            Never-married          ?       Own-child               White   \n",
       "221                 Divorced          ?   Not-in-family               White   \n",
       "226                 Divorced          ?   Not-in-family  Amer-Indian-Eskimo   \n",
       "243            Never-married          ?       Own-child               White   \n",
       "266            Never-married          ?       Own-child               White   \n",
       "297       Married-civ-spouse          ?            Wife  Asian-Pac-Islander   \n",
       "312            Never-married          ?       Own-child               Black   \n",
       "326            Never-married          ?       Own-child               Black   \n",
       "346       Married-civ-spouse          ?         Husband               White   \n",
       "347                 Divorced          ?       Unmarried               White   \n",
       "354       Married-civ-spouse          ?         Husband               White   \n",
       "397            Never-married          ?  Other-relative               White   \n",
       "408       Married-civ-spouse          ?            Wife               White   \n",
       "430                  Widowed          ?   Not-in-family               White   \n",
       "431            Never-married          ?       Own-child               White   \n",
       "449            Never-married          ?       Own-child               White   \n",
       "459       Married-civ-spouse          ?         Husband               White   \n",
       "471            Never-married          ?       Own-child               White   \n",
       "484            Never-married          ?  Other-relative               Black   \n",
       "486       Married-civ-spouse          ?         Husband               White   \n",
       "...                      ...        ...             ...                 ...   \n",
       "32062          Never-married          ?       Own-child  Asian-Pac-Islander   \n",
       "32070          Never-married          ?       Own-child               White   \n",
       "32073     Married-civ-spouse          ?         Husband               White   \n",
       "32075          Never-married          ?   Not-in-family               Other   \n",
       "32080          Never-married          ?       Own-child               White   \n",
       "32083               Divorced          ?       Unmarried               Black   \n",
       "32088          Never-married          ?  Other-relative  Asian-Pac-Islander   \n",
       "32093     Married-civ-spouse          ?         Husband               White   \n",
       "32103              Separated          ?       Unmarried               Black   \n",
       "32189     Married-civ-spouse          ?            Wife               White   \n",
       "32201     Married-civ-spouse          ?            Wife               White   \n",
       "32206                Widowed          ?   Not-in-family               White   \n",
       "32275          Never-married          ?       Own-child               White   \n",
       "32291          Never-married          ?   Not-in-family               Black   \n",
       "32304     Married-civ-spouse          ?            Wife               Black   \n",
       "32310          Never-married          ?       Own-child               White   \n",
       "32314          Never-married          ?       Own-child               White   \n",
       "32317               Divorced          ?   Not-in-family               White   \n",
       "32335          Never-married          ?       Own-child               White   \n",
       "32343                Widowed          ?       Unmarried               White   \n",
       "32426          Never-married          ?       Own-child               White   \n",
       "32477               Divorced          ?  Other-relative               Black   \n",
       "32490               Divorced          ?   Not-in-family               Black   \n",
       "32494          Never-married          ?   Not-in-family               White   \n",
       "32525               Divorced          ?       Unmarried               White   \n",
       "32530     Married-civ-spouse          ?            Wife               White   \n",
       "32531          Never-married          ?   Not-in-family  Asian-Pac-Islander   \n",
       "32539     Married-civ-spouse          ?         Husband               White   \n",
       "32541              Separated          ?   Not-in-family               Black   \n",
       "32542     Married-civ-spouse          ?         Husband               White   \n",
       "\n",
       "          sex  capital-gain  capital-loss  hours-per-week native-country  \\\n",
       "27       Male             0             0              60          South   \n",
       "61       Male             0             0              40            NaN   \n",
       "69       Male             0             0              40  United-States   \n",
       "77       Male             0             0               2  United-States   \n",
       "106    Female         34095             0              32  United-States   \n",
       "128      Male             0             0              40  United-States   \n",
       "149    Female             0             0              40  United-States   \n",
       "154      Male             0             0              45  United-States   \n",
       "160    Female             0             0              20  United-States   \n",
       "187    Female             0             0              50  United-States   \n",
       "201      Male             0             0              40          Italy   \n",
       "221      Male             0             0              40  United-States   \n",
       "226    Female             0             0              10  United-States   \n",
       "243      Male             0             0              20  United-States   \n",
       "266      Male             0             0              24         Canada   \n",
       "297    Female          3464             0              40            NaN   \n",
       "312    Female             0             0              25  United-States   \n",
       "326    Female             0             0              15  United-States   \n",
       "346      Male             0             0               8  United-States   \n",
       "347    Female             0             0              40  United-States   \n",
       "354      Male             0             0              40  United-States   \n",
       "397    Female             0             0              35  United-States   \n",
       "408    Female             0          1887              40  United-States   \n",
       "430      Male             0             0              24  United-States   \n",
       "431      Male             0             0              40  United-States   \n",
       "449    Female             0             0               5  United-States   \n",
       "459      Male             0             0              40  United-States   \n",
       "471      Male             0             0              40  United-States   \n",
       "484      Male           594             0              40  United-States   \n",
       "486      Male             0             0              35  United-States   \n",
       "...       ...           ...           ...             ...            ...   \n",
       "32062    Male             0             0              40    Philippines   \n",
       "32070  Female             0             0              20  United-States   \n",
       "32073    Male             0             0              25  United-States   \n",
       "32075  Female             0             0              40          Japan   \n",
       "32080    Male             0             0              42  United-States   \n",
       "32083  Female             0             0              40  United-States   \n",
       "32088  Female             0             0              24  United-States   \n",
       "32093    Male             0             0              40         Canada   \n",
       "32103  Female             0             0              40  United-States   \n",
       "32189  Female             0             0              36  United-States   \n",
       "32201  Female             0             0              60  United-States   \n",
       "32206  Female             0             0              40  United-States   \n",
       "32275  Female             0             0              30  United-States   \n",
       "32291  Female             0             0              40  United-States   \n",
       "32304  Female             0             0              40  United-States   \n",
       "32310    Male             0             0              40  United-States   \n",
       "32314    Male             0             0               4  United-States   \n",
       "32317  Female             0             0               5  United-States   \n",
       "32335  Female             0             0              25  United-States   \n",
       "32343  Female             0             0              40  United-States   \n",
       "32426    Male             0             0              40  United-States   \n",
       "32477    Male             0             0              40  United-States   \n",
       "32490    Male             0             0              35  United-States   \n",
       "32494    Male             0             0               3  United-States   \n",
       "32525  Female             0             0               1            NaN   \n",
       "32530  Female             0             0              55  United-States   \n",
       "32531  Female             0             0              99  United-States   \n",
       "32539    Male             0             0              10  United-States   \n",
       "32541  Female             0             0              32  United-States   \n",
       "32542    Male             0             0              25  United-States   \n",
       "\n",
       "      income  \n",
       "27      >50K  \n",
       "61     <=50K  \n",
       "69     <=50K  \n",
       "77     <=50K  \n",
       "106    <=50K  \n",
       "128    <=50K  \n",
       "149    <=50K  \n",
       "154     >50K  \n",
       "160    <=50K  \n",
       "187    <=50K  \n",
       "201    <=50K  \n",
       "221    <=50K  \n",
       "226    <=50K  \n",
       "243    <=50K  \n",
       "266    <=50K  \n",
       "297    <=50K  \n",
       "312    <=50K  \n",
       "326    <=50K  \n",
       "346    <=50K  \n",
       "347    <=50K  \n",
       "354     >50K  \n",
       "397    <=50K  \n",
       "408     >50K  \n",
       "430    <=50K  \n",
       "431    <=50K  \n",
       "449    <=50K  \n",
       "459    <=50K  \n",
       "471    <=50K  \n",
       "484    <=50K  \n",
       "486    <=50K  \n",
       "...      ...  \n",
       "32062  <=50K  \n",
       "32070  <=50K  \n",
       "32073  <=50K  \n",
       "32075  <=50K  \n",
       "32080  <=50K  \n",
       "32083  <=50K  \n",
       "32088  <=50K  \n",
       "32093  <=50K  \n",
       "32103  <=50K  \n",
       "32189  <=50K  \n",
       "32201  <=50K  \n",
       "32206  <=50K  \n",
       "32275  <=50K  \n",
       "32291  <=50K  \n",
       "32304  <=50K  \n",
       "32310  <=50K  \n",
       "32314  <=50K  \n",
       "32317  <=50K  \n",
       "32335  <=50K  \n",
       "32343  <=50K  \n",
       "32426  <=50K  \n",
       "32477  <=50K  \n",
       "32490  <=50K  \n",
       "32494  <=50K  \n",
       "32525  <=50K  \n",
       "32530   >50K  \n",
       "32531  <=50K  \n",
       "32539   >50K  \n",
       "32541  <=50K  \n",
       "32542  <=50K  \n",
       "\n",
       "[1843 rows x 15 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['occupation'] == '?']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df['occupation'].str.contains('?', regex=False)].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [age, workclass, fnlwgt, education, education-num, marital-status, occupation, relationship, race, sex, capital-gain, capital-loss, hours-per-week, native-country, income]\n",
       "Index: []"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['occupation'] == '?'] # Sweet, finally worked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Convert `income` to an ordered categorical type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you'll see, `income` has two values `<=50K` and `>50K`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<=50K    23068\n",
       ">50K      7650\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income'].value_counts() #value counts shows the two categorical values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your job is to turn them into an **ORDERED** categorical type with the following order: `['<=50K', '>50K']`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expected result:\n",
    "df['income'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html\n",
    "# import\n",
    "# instead of passing 'category', \n",
    "# use an instance of CategoricalDtype.\n",
    "from pandas.api.types import CategoricalDtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initially it's dtype('O') # whatever that means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an ordered data type based on the string values found in value_counts()\n",
    "cat_type = CategoricalDtype(['<=50K', '>50K'], ordered=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['income'] = df['income'].astype(cat_type) # Cast a pandas object to a specified dtype dtype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<=50K    23068\n",
       ">50K      7650\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income'].astype(cat_type).value_counts() # working as prescribed in the solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<=50K    23068\n",
       ">50K      7650\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income'].value_counts() # is astype required here?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Create a dummy variable _\">50K\"_ or _\"<=50K\"_ and concatenate them to our dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Note: This means it turns the thing into 0s and 1s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   <=50K  >50K\n",
       "0      1     0\n",
       "1      1     0\n",
       "2      1     0\n",
       "3      1     0\n",
       "4      1     0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df['income']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  fnlwgt  education  education-num  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       marital-status         occupation   relationship   race     sex  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week native-country income  <=50K  \\\n",
       "0          2174             0              40  United-States  <=50K      1   \n",
       "1             0             0              13  United-States  <=50K      1   \n",
       "2             0             0              40  United-States  <=50K      1   \n",
       "3             0             0              40  United-States  <=50K      1   \n",
       "4             0             0              40           Cuba  <=50K      1   \n",
       "\n",
       "   >50K  \n",
       "0     0  \n",
       "1     0  \n",
       "2     0  \n",
       "3     0  \n",
       "4     0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df, pd.get_dummies(df['income'])], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Step above added the two new categorical data columns!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  fnlwgt  education  education-num  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       marital-status         occupation   relationship   race     sex  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week native-country income  <=50K  \\\n",
       "0          2174             0              40  United-States  <=50K      1   \n",
       "1             0             0              13  United-States  <=50K      1   \n",
       "2             0             0              40  United-States  <=50K      1   \n",
       "3             0             0              40  United-States  <=50K      1   \n",
       "4             0             0              40           Cuba  <=50K      1   \n",
       "\n",
       "   >50K  \n",
       "0     0  \n",
       "1     0  \n",
       "2     0  \n",
       "3     0  \n",
       "4     0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expected result:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Plot a **distplot** showing the distribution of `age`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1185fcdd8>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Expected output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce3db5e828>"
      ]
     },
     "execution_count": 41,
     "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.distplot(df['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Plot a **boxplot** showing the distribution of `age`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118213f98>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xr3evFzd79uzMnj07SbJy5crMmTMnd9xxh/u8sC1btuTYsWN5+OGH84tf/CLf+ta30tfX5z4vbNOmTens7MymTZvy4osvZtWqVdm3b1+p+9y3pUbAVVddlS9+8YtJkmazmfb29vzmN7/J5ZdfniSZOXNmfvnLX7ZyRIbZBz/4waxatSpJ8tJLL2XixIl2PgqsWbMmn/zkJ3PeeecliZ2PAjt37syRI0cyf/78zJs3L9u2bcvRo0dz4YUXptFoZMaMGfZezFNPPZV3vOMdWbRoUT73uc/l/e9/v3t9lNixY0deeOGFXHPNNe7z4qZOnZr+/v4cP348hw8fTkdHh/u8uBdeeCEzZ85MknR1dWXHjh3l7nOhPwLGjRuX8ePH5/Dhw7nxxhtz0003pdlsptFoDDx/6NChFk/JcOvo6Mgtt9ySVatWZdasWXZe3ObNm3POOefkfe9738DH7Ly+M888M5/5zGdy//33Z+XKlVm6dGnOOuusgeftvZ7u7u78+te/zj333JOVK1dm8eLF7vVRYt26dVm0aFEOHz488OM6iZ1X1NnZmd27d+fqq6/O7bffnrlz57rPi7vooovyxBNPpNlsZvv27Tl06FA6OzsHnq+wc2/dHyF79uzJokWLcv3112fWrFlZu3btwHM9PT2ZOHFiC6djpKxZsyaLFy/Oddddl97e3oGP23k9P/rRj9JoNPL000/nueeeyy233JL9+/cPPG/nNU2dOjVvfvOb02g0MnXq1EyYMCEHDhwYeN7e65k0aVK6uroyZsyYdHV1ZezYsfnzn/888Lyd13Tw4MHs2rUr733ve3P48OH09PQMPGfn9Xz/+9/PjBkz8uUvfzl79uzJpz/96fT19Q08b+f1zJkzJ7/73e9y/fXX59JLL820adNy5MiRgecr7NyJ/gjYt29f5s+fnyVLluRjH/tYkuTiiy/O1q1bkyRPPvlk3vOe97RyRIbZI488knXr1iVJzjrrrDQajVxyySV2XtjGjRvzwAMPZMOGDbnooouyZs2azJw5086L++EPf5ivfe1rSZK//OUvOXLkSDo7O/PHP/4xzWYzTz31lL0X8+53vzs///nP02w2B3Y+ffp093px27Zty/Tp05Mk48ePzxlnnOE+L2zixImZMGFCkuTss8/OsWPHfO1e3I4dOzJ9+vQ89NBDueqqq/KWt7yl3H3eaDabzVYPUc3q1avzk5/8JF1dXQMf+8pXvpLVq1enr68vXV1dWb16ddrb21s4JcPplVdeydKlS7Nv374cO3YsCxYsyFvf+tbcfvvtdj4KzJ07NytWrEhbW5udF/fqv9L70ksvpdFoZPHixWlra8tdd92V/v7+zJgxIzfffHOrx2SY3X333dm6dWuazWZuvvnmTJkyxb1e3H333ZeOjo7ccMMNSZLt27e7zwvr6enJsmXLsnfv3vT19WXevHm55JJL3OeF7d+/P1/60pdy5MiRTJgwIXfeeWf27NlT6j4X+gAAAFCIt+4DAABAIUIfAAAAChH6AAAAUIjQBwAAgEKEPgAAABQi9AEAAKAQoQ8AAACFdLR6AABgeBw7diwrVqzIb3/72+zbty9Tp07Nd77znWzatCkPPPBAJkyYkK6urlx44YX5whe+kCeffDL33ntvjh07lilTpmTVqlWZPHlyq38ZAMA/yIk+ABTxzDPP5IwzzsgPfvCD/OxnP0tvb2/uu+++bNy4MZs3b86DDz6YP/zhD0mS/fv35xvf+Ebuv//+PPLII5kxY0a+/vWvt/hXAAAMByf6AFDEZZddlkmTJmXjxo158cUX8/vf/z5XXHFFrrzyyowfPz5Jcs011+TgwYP51a9+lT179mTevHlJkuPHj+fss89u5fgAwDAR+gBQxGOPPZZ777038+bNy+zZs9Pd3Z0JEybk4MGD/+fa/v7+XHrppfne976XJOnt7U1PT8+pHhkAGAHeug8ARTz99NO5+uqrM2fOnLzhDW/Itm3bkiRbtmzJ4cOHc/To0fz0pz9No9HIu971rmzfvj27du1Kknz3u9/N3Xff3crxAYBh0mg2m81WDwEA/OOef/75LF68OO3t7RkzZkzOP//8dHV15bzzzsuDDz6Yzs7OTJ48OZdddlkWLFiQxx9/PPfcc0+OHz+e888/P2vXrvWP8QFAAUIfAArbtWtXtmzZkhtuuCFJsnDhwnz84x/PBz7wgdYOBgCMGD+jDwCFXXDBBdmxY0c+/OEPp9FoZMaMGbnyyitbPRYAMIKc6AMAAEAh/jE+AAAAKEToAwAAQCFCHwAAAAoR+gAAAFCI0AcAAIBChD4AAAAU8j9MDSh3/6N7agAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Expected output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce3cec7e10>"
      ]
     },
     "execution_count": 45,
     "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": [
    "# box plot of age\n",
    "sns.boxplot(data=df, x='age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Remove outlier values from `age` and plot the previous boxplot again.\n",
    "\n",
    "_Suppose values higher than 70 as outliers_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117d0f128>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Expected solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df[df['age'] <= 70] # remove ages above 70 years old by restricting the dataset in df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce3cde3c88>"
      ]
     },
     "execution_count": 47,
     "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.boxplot(data=df2, x='age') # plot df2 age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: How many people work more than 20 hours per week?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29633"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expected output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28295"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['hours-per-week'] > 20, 'hours-per-week'].count() # filter for hours above 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: How many men and women are? Show totals and proportion over all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male count: 21790\n",
      "Female count: 10771\n",
      "Male proportion: 0.6692054912318418\n",
      "Female proportion: 0.33079450876815825\n"
     ]
    }
   ],
   "source": [
    "# Expected output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male count: 20788\n",
      "Female count: 9930\n",
      "Male proportion: 0.6767367667165831\n",
      "Female proportion: 0.3232632332834169\n"
     ]
    }
   ],
   "source": [
    "# test of code\n",
    "sex_column = df[\"sex\"]\n",
    "\n",
    "num_male = sex_column[sex_column == 'Male'].count()\n",
    "print(\"Male count: {}\".format(num_male))\n",
    "\n",
    "num_female = sex_column[sex_column == 'Female'].count()\n",
    "print(\"Female count: {}\".format(num_female))\n",
    "\n",
    "total_rows = len(sex_column)\n",
    "\n",
    "male_prop = num_male / total_rows\n",
    "print(\"Male proportion: {}\".format(male_prop))\n",
    "\n",
    "female_prop = num_female / total_rows\n",
    "print(\"Female proportion: {}\".format(female_prop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Plot a bar plot showing the income by gender\n",
    "\n",
    "_Hint: Check [countplots](https://seaborn.pydata.org/tutorial/categorical.html#bar-plots) in Seaborn docs_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f186160>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Expected Result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce3cdcb320>"
      ]
     },
     "execution_count": 56,
     "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": [
    "# x axis is income, hue/category is sex\n",
    "sns.countplot(x='income', hue='sex', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Create a bar plot showing count of `race`s\n",
    "\n",
    "It should look like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1108a8320>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Expected result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce3cd344e0>"
      ]
     },
     "execution_count": 57,
     "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": [
    "# x axis is race\n",
    "sns.countplot(x='race', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Count per `race`\n",
    "\n",
    "Using grouping and aggregation functions, count how many people are per each race:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>race</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>1039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Black</td>\n",
       "      <td>3124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Other</td>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>White</td>\n",
       "      <td>27816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 race  count\n",
       "0  Amer-Indian-Eskimo    311\n",
       "1  Asian-Pac-Islander   1039\n",
       "2               Black   3124\n",
       "3               Other    271\n",
       "4               White  27816"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expected result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amer-Indian-Eskimo</th>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian-Pac-Islander</th>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "      <td>895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "      <td>2817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "      <td>25933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      age  workclass  fnlwgt  education  education-num  \\\n",
       "race                                                                     \n",
       "Amer-Indian-Eskimo    286        286     286        286            286   \n",
       "Asian-Pac-Islander    895        895     895        895            895   \n",
       "Black                2817       2817    2817       2817           2817   \n",
       "Other                 231        231     231        231            231   \n",
       "White               25933      25933   25933      25933          25933   \n",
       "\n",
       "                    marital-status  occupation  relationship    sex  \\\n",
       "race                                                                  \n",
       "Amer-Indian-Eskimo             286         286           286    286   \n",
       "Asian-Pac-Islander             895         895           895    895   \n",
       "Black                         2817        2817          2817   2817   \n",
       "Other                          231         231           231    231   \n",
       "White                        25933       25933         25933  25933   \n",
       "\n",
       "                    capital-gain  capital-loss  hours-per-week  \\\n",
       "race                                                             \n",
       "Amer-Indian-Eskimo           286           286             286   \n",
       "Asian-Pac-Islander           895           895             895   \n",
       "Black                       2817          2817            2817   \n",
       "Other                        231           231             231   \n",
       "White                      25933         25933           25933   \n",
       "\n",
       "                    native-country  income  <=50K   >50K  \n",
       "race                                                      \n",
       "Amer-Indian-Eskimo             286     286    286    286  \n",
       "Asian-Pac-Islander             895     895    895    895  \n",
       "Black                         2817    2817   2817   2817  \n",
       "Other                          231     231    231    231  \n",
       "White                        25933   25933  25933  25933  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('race').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race\n",
       "Amer-Indian-Eskimo      286\n",
       "Asian-Pac-Islander      895\n",
       "Black                  2817\n",
       "Other                   231\n",
       "White                 25933\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('race')['age'].count() # just show age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Create a barplot with the `occupation` column\n",
    "\n",
    "We want to check the number of people for each `occupation`. In this case, we want to see a nice, sorted plot like this one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x107e25ba8>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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PKtubGwX9KR/9KR/9KRu9KR/9KR/9KV/l+5NbpXVcj37NtnNMl6uwkusTry1Ut2oNJZo3b66mTZvKZrOpefPm8vHx0fnz55235+TkyNfXV7m5ucrJyXFOdzgc5QYSknTu3CW31f1bkZGRZXUJ1zX6UzZ6Uz76Uz76Uz76UzZ6Uz76Uz76Uz76UzZ6Uz76A3cpK/Cq1l/fWLt2rWbNmiVJ+umnn3T58mXVrl1bJ0+elDFG27dvV0BAgPz9/bVt2zZJ0r59+/SHP/yhOssEAAAAAADVoFrPlOjfv7/Gjx+vIUOGyGazacaMGfLw8NC4ceNUVFSkzp0765577tHdd9+tHTt2aPDgwTLGaMaMGdVZJgAAAAAAqAbVGkrUqlVLc+fOLTV9zZo1Jf728PDQ3/72t+oqCwAAAAAAWKBaL98AAAAAAAAoRigBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsQSgBAAAAAAAsYbe6AAAAAADAb1vRB5lWl1AtPIfXs7qEGoczJQAAAAAAgCUIJQAAAAAAgCUIJQAAAAAAgCUIJQAAAAAAgCUIJQAAAAAAgCUIJQAAAAAAgCUIJQAAAAAAgCXsVhdQFofDoSlTpujw4cOqVauWpk2bpqZNm1pdFgAAAAAAqCLX7ZkSW7ZsUX5+vlavXq1XX31Vs2bNsrokAAAAAABQha7bMyUSExPVpUsXSdK9996rAwcOWFwRAAAAAADXzrHya6tLqBYeQ+675mVsxhjjhlp+tQkTJqhnz556+OGHJUldu3bVli1bZLdftzkKAAAAAAC4Btft5Rve3t7Kyclx/u1wOAgkAAAAAACoQa7bUMLf31/btm2TJO3bt09/+MMfLK4IAAAAAABUpev28o3iX984cuSIjDGaMWOGWrZsaXXdBoISAAAgAElEQVRZAAAAAACgily3oQQAAAAAAKjZrtvLNwAAAAAAQM1GKAEAAAAAACxR40OJhIQEPfjggwoODlZwcLAGDhyoZcuWVWjZ/fv3q0ePHpo7d66bq5S2bdum1atXl3n7/PnztXLlSklSbGys2+s5evSo/vrXvyo4OFh//vOfNW/ePFX0Sp+xY8fqz3/+s44dO6a4uDg3VyplZGRoypQplV7+l9tIcHCwXn755aor8Dp3tf4lJCRozJgxFV7f0qVL1blzZ+Xl5ZW6beXKlZo/f35lyixl/fr1evPNNys073fffacFCxa4bf3utmTJEg0fPlxDhw5VcHCwDhw44HK+1NRUDRw4sJqrq7yUlBSFhIQoODhYgwcP1pQpU5Sdna309HR9/vnnkqTg4GAdO3bM4kpLeumllyq1XEJCgu666y59/PHHJab37dtX4eHhFV5PWdvmmDFjlJ+fX6nafq0lS5bom2+++dXrmTVrloKDg/XYY4+pa9euzvF49erVKigoqIJKf53PPvtMP/30k1vvY/r06UpPTy813dVY/Oabb2r9+vWVvq8xY8YoISGhWsY7q17Lqamp8vf3L7GPv9b9QVmq4jUXGxurXr16acOGDeXui131rzL7tqsp6zVYUUOGDFFycnKV1lSVVqxYIYfDcU3L/Jr3EocPH9ZXX311TfeXl5fnPH5ev3694uPjr2n56lKRvlzt/c2Vrsf9fVUr7xi5Khw7dkzBwcGlprt6r1PR56W63BC/sfnAAw8oJiZGkpSfn6/HHntMTzzxhHx9fctd7t///reGDRvm8smtaoGBgRWed/HixRo6dKjbarl48aLGjh2r+fPnq1mzZioqKtIrr7yiVatWaciQIVddfufOndq9e7dSU1MVFxenAQMGuK1WSWrQoMGvCiWkktvIjaYq+neljRs3qnfv3vr444/11FNPVdl6f402bdqoTZs2VpdRKd9//70+//xzrVy5UjabTd99953CwsK0ceNGq0v7VXJzc/Xiiy9q2rRpuueeeyRJGzZs0KuvvqqgoCAdP35c3bp1s7hK137Nm4AWLVro448/Vp8+fST9fMB6+fLlKqnLyjHsr3/9a5WspzicWb9+vY4fP65x48ZJkrp166Y//elPVXIfv8b//u//asqUKWrYsKHb7mPChAluW/eN6s4776zwm8hrURWvuc2bN+v//b//p7vuuktPPvnkNS3rjn1bWa/BmuLtt9/WwIED5eFxbZ/LVva9xObNm1W/fn398Y9/rPB9ZWRkOI+fr5fjqLJcrS/X8v7mRmDlMfL1/l7nhgglrpSdnS0PDw8NHz5ct99+uy5cuKAlS5YoIiJCqampKioq0nPPPacmTZpo/fr18vLy0u9//3v16NHDuY7ExETNnj1bdrtdN998s9566y1t3rxZW7ZsUU5Ojs6dO6fRo0crKChIe/bsUUxMjDw9PXX77bfrb3/7m4qKijR+/Hilp6eroKBAkyZN0okTJ5yD/9y5c3XgwAGdP39erVu31syZM533vXjxYl24cEFTpkxRVlaW+vbtq65du+rYsWOaPXu2lixZ8qt7FB8fr06dOqlZs2aSJE9PT82ePVtff/21BgwYIC8vLw0cOFA33XSTli9frsLCQtlsNi1YsEDz5s1Tdna2Ro0apVtvvVXff/+9FixYUOKTxdTUVI0aNUp+fn4KDAxUYGCgpk2bJkny8/PTjBkzdPDgQb399tvy8PBQRkaGBg0apGeeeUZ79uzRggULZIxRTk6O5s6dKy8vL40dO1Zr1qzR448/rmbNmsnLy+tXvfAKCws1dOhQjR49Wm3atNGzzz6rd999VxcvXixVq7e3t6KiovTNN9+ooKBAISEh6t69e4n19e3bVwEBATp8+LBatGihW2+9VXv37lWtWrW0ZMkSnT17VlOmTFFeXp4yMjIUGhqq7t27q2/fvurYsaMOHz4sm82mRYsWqXbt2oqMjNSPP/6o06dPq1u3bhozZoySk5MVHh4uu92u//qv/1JaWpqWLVumTz75RB988IE8PDx0//33a9y4cZo/f76+/vprXbp0SdOnT9f48eO1Zs0affrpp6We02uRkJCgO+64Q4MHD9Zrr72mp556Snv37tWMGTPk6+srT09P3XvvvUpNTdWYMWPUqFEjpaamqk+fPjp69KgOHjyorl27auzYsSXWm5ubW+o1c6Vly5bpo48+ks1mU+/evTVs2DCFh4fr/PnzOn/+vEaOHKlNmzYpJiZGcXFxWrlypRwOh7p166aXX35ZsbGx2rx5sy5fvqy6detW+SdPv4aPj4/S09O1du1aBQYGqk2bNlq7dm2Zr4Virsae1NRUjR8/Xna7XQ6HQ3PnzlWjRo0seVxbt27VH//4R2cgIUlPPvmkYmNjFRkZqfr16+u+++6TJC1cuFBnzpzR5cuXFR0drdtvv11z587V3r175XA4NHz4cPXq1UvBwcGqV6+eLly4oP/5n/+Rp6enc93jx49XcnKycnNzNWzYMP3pT39y2aN//vOfWrdunRwOh0aOHKn4+HjnGPzkk0/q3XffVb9+/bRjxw7t379fM2bMkMPhUMOGDfXmm28qOTm51Bjh4+PjrKN169Y6ceKEsrKy5OPjo40bN6pv3746deqUy+3Q4XC43Pb379+vESNGKDMzU0OGDNGgQYPUrVs3ffLJJ5o8ebJq1aqltLQ0nT59WrNmzVK7du1cjgW/9N577+njjz+W3W5XQECAXnvtNc2fP1/Hjx/X2bNndfHiRU2cOFEBAQEllgsPD1fv3r115swZffnll8rNzdXJkyf1l7/8RU899ZSWL1+uDz/8UB4eHrr77rs1ceJEhYeHyxijU6dO6dKlS5o9e7ZatmypRYsWKS4uTrm5uWrSpImSk5OVnp6uLl26KCIiwuW+5+jRo1q6dKm8vLyUmpqq3r17a9SoUdq8ebOWLl0qu92u2267TTExMVq4cKHLx7Nx40b9/e9/V61atdSsWbNS28Pzzz/vDAVXrFihWrVqOR9/cHCw7rrrLh09elS1a9dWQECAtm/frosXL+q9995T7dq1NX78+BLHGQ888ICeeeYZbdq0STabTX/729/04IMPOoOPTZs2KTU1VWfPnlV6errzDesXX3yhefPmydvbW6dPn1aLFi2UmJhYap8QHh7ucjtYvny54uLi1KBBA509e7bUNnC18XTRokUKDQ2VMUZ5eXmaOnVqqTfGrl4bxbKysjRhwgSdO3dOkjRx4kTdddddLrf/jz76yOX2dPjwYZfHDW+++abzGOXEiRNKSEjQpUuXdPHixRL1FRUVudyP/nK/sWrVKnl5eenHH3/U4MGDtXv3bh06dEjDhg3T008/fdXXXFxcnJYvX65bbrlFXl5e6t27d4k3IqtXr9bBgwc1YcIExcTE6NVXX9WaNWsUExOjhIQEFRYWqmfPns7Q75djYXp6ulatWqWYmBj17NlT/v7+OnHihG699VbNnz9fBQUFev3113X69Gk1atRIX331lbZv317qOa+IN954Q19//bVzbOzZs6e+/vprzZw5U8YYNWrUSHPmzJEkzZs3TxkZGcrNzVV0dLSaNGlSYl1vvvmm9u7dq4KCAvXu3VsjR47UkCFDNGvWLDVt2lSxsbG6ePGi+vTpo3HjxunWW2/Vjz/+qEceeUSvvPKKxo0bJ7vdrvT0dF2+fFlvvPGGmjdvrqVLl+rTTz+V3W5Xp06dNHbsWMXExOjbb7/VpUuX9OSTTyozM1Njx47VvHnzKtUHqeLvJe6//35t2LBBXl5eateunXJzcyv0nmDdunXO42djjOrXr68ffvhBrVu31pNPPqmMjAw9//zzWr9+vct94ZVOnDjhcr9/tX1onTp19Oyzz6pjx4769ttvtWjRIi1evPia+9KnTx8lJyfrtttu08WLF/XSSy8pPz9f/fr108aNG0uMo5Lr/f2sWbOUmJgoSXr88cf17LPPOvc5gYGB2rZtmzZt2qRZs2ZVeF9/5fGSVLXHCLfeeqvL/rg6Rr6y53369NG2bduUm5urjIwMDRs2TPHx8Tp69Khef/11de/e3eV+/PTp0xo3bpyMMWrQoME1bcsFBQWl9k29e/cuUdeiRYs0YcKEEtto+/btNXnyZCUnJ8vhcCg0NFSdOnUqc+yqEFPD7d692zzwwANm6NChJjg42IwYMcJs3brVDB061GzevNkYY8yyZcvM9OnTjTHGZGVlmR49epizZ8+aefPmmRUrVpRa56xZs8x7771nioqKzGeffWbS0tLMunXrzPDhw01RUZHJyMgwXbt2Nfn5+aZnz57mzJkzxhhjYmJizOrVq837779v5syZY4wx5sSJE+b9998369atM3PmzDFZWVlmyZIlxhhjioqKzGOPPWZ+/PHHErU89NBDxhhjdu3aZV5++WVnTf/617+qpGdvv/22+eCDD1z2sm/fvs6/Fy9ebC5dumSMMWbSpEnmH//4R4n6UlJSzIABA0qtJyUlxXTq1Mnk5eUZY4wZMGCAOXr0qDHGmDVr1pjo6Gize/du06tXL5OXl2cuX75sunfvbs6cOWNiY2PNjz/+6Lz/RYsWlbifRx55xCQlJV3T471yGyn+t3TpUpOSkmL69Oljhg8fbrZu3VpmrZs3bzahoaHGGGPOnz9vYmJiSt3HI488Yvbu3WuMMSYoKMi5vmeeecYcPHjQ7Nixw+zevdsYY0xiYqIZPny4c7nExERjjDFjx441H330kUlJSTFr1qwxxhiTm5trOnbsaIwx5sUXX3Sud/Xq1Wbo0KHm3LlzplevXs7nady4cWb79u1m3rx5JioqqtTz5Oo53b17t/PxXc2rr75qvvjiC2OMMYMHDzb79u0zjz/+uDl+/LgxxpjIyEgzb9485zZw8eJFc/r0aXP33Xebc+fOmdzcXPPggw+WWm95r5mjR4+awYMHm8LCQlNYWGiCg4PNsWPHTFhYmHn//fedz3FoaKg5c+aM6dGjh7l8+bJxOBzO19z8+fNNUVGRMcaYESNGmL179zrXfz04cOCACQ8PNw8//LAJCgoyn376abmvBYfD4XLsiY2NNdOnTzf5+flm586d5vDhw5Y9piVLljifnyuNGTOmRO+HDh1qPvzwQ2OMMfPmzTNLliwxW7dudW6Tubm5pl+/fubChQslxvUrZWVlmUcffdScPXvWnD171mzcuLHMHq1bt8688MILxhhjCgsLTVBQkMnJyTH79+83ISEhxpj/G+P69etnvv/+e2PMz+PBgQMHXI4RxYq3w4ULF5q1a9cah8Nhhg4dar788kvz2muvudwOy9r2hw8fbhwOh0lJSTG9evUyxvw8XuTm5pqwsDCzePFiY8zPY8GkSZPKHAuudOjQIdO/f3+Tn59vHA6HGT16tPn888/NvHnzTHh4uDHGmCNHjpTYDxQLCwszX375pVm3bp0ZMWKEs96goCBjjDFPPfWU2b9/vzHGmOXLl5uCggITFhZm5s+fb4wxZuvWreb55583SUlJZtCgQSYuLs7MmjXLzJw50+zatcu0bdvW5ObmGmPKHqd69eplCgoKTE5OjvH39zfGGBMSEmI++eQTY4wxGzZsMBcuXHD5eDIzM0337t1NVlaWMcaY6dOnm2XLlpXYHoq3x+Ln/EpDhw517gNHjBhhYmNjjTHGvP766+azzz4r8zjjlVdeMXv27DF5eXmmd+/epqCgwHkf8+bNMxMnTjTGGLN9+3bz1FNPmQceeMC0b9/eDBo0yAwdOtR06NDBjBo1yuU+wdV2kJGRYXr27Gny8vJMfn6+efzxx83u3buvaTz94osvTEhIiLl8+bL59ttvnfu2K7l6bRQ/rjfeeMMsX77cuY0MHjzYFBUVlTkOu9qeyjpuuHLbfOSRR0xKSoo5fvy4ufvuu0vs4/fu3Vtmz67cb/Tu3dvk5+ebr7/+2gQGBpq8vDxz8uRJ069fP+d9lPWaO3v2rOnZs6e5dOmSKSwsNE8//bRZt26dy23n+++/L3Usk5KSYvLy8szKlSud8/1yLLxy/9y6dWuTnp5ujDFm0KBB5uuvvzYffPCBmT17tjHGmO+//960bt261P2X5cpxOD4+3owdO9YYY8zly5fN448/brKysszjjz9ujh075nzc3333nRk8eLD56KOPjDHGREdHm/fee6/Uuh9++GGTlpZmcnNzzerVq40xPx8z/PDDD8aYn4/LFy5caH744Qfz4IMPmgsXLpjCwkLz5z//2Xz33Xfm1VdfdfZ7y5YtZvTo0c6xo6CgwDgcDvPCCy+YL7/80kRHR5uZM2c677tLly6moKCgwn0wpmreS5S1z3E1xl+5LRQvf/ToURMcHGyMMWbp0qVm2bJlZe4Lr+Rqv1+RfejWrVud4+SUKVPM559/Xqm+FG9H58+fN7179zYOh8Ns2rTJzJo1q9T6XG3jn3/+uRk9erRxOBwmPz/f9O/f3xw6dMi5zzHGmC+//NKEhYVd077+SlV9jFAWV8fIv+zVc889Z4wx5qOPPjL9+/c3DofD7Nq1y4waNarM/fjUqVOdj+njjz82Q4cOLfe5Kv5XWFhY5nZ7ZV2uttHly5ebN954wxhjTGZmpundu7cxxvXYVVE3xJkSrk5Xeffdd9W8eXNJP19/89BDD0mSvL291bJlS6WkpDjnTU5O1sSJEyVJ/fr10wsvvKC3335bzz77rBo2bKgOHTpIkv74xz/Kw8ND9evXl6+vr06fPq3Tp08rNDRU0s+f9j700EM6d+6c83SmZs2aafjw4c5rQn/3u985U9zatWvr0qVLZV5H26lTJ02bNk2ZmZnasWNHqU+XK6tx48Y6ePBgiWkpKSn66quvnD2TpFtvvVVhYWGqU6eOjh8/rnvvvbfMdU6YMEEnT55U3bp19frrr6tJkybOdPTYsWOaOnWqpJ8Tu+IzNO677z7nPK1atdLJkyfVsGFDTZ8+XbVr19ZPP/0kf3//Uvd1ZY0VVdYpTf7+/tq3b5/z+XJVa506dZyP/ZZbblFoaKhiY2P1r3/9S5KcnxC1a9dOkuTr66uWLVs6/5+Xl6cGDRpo8eLFWrt2rWw2mwoLC501tG3bVpLUqFEj5eXlyc/PT99++612794tb29v5/Wsx44dc36yfP/99+uf//ynTp48qczMTGdSmZOTo5MnT5bZp2t5Tn/pwoUL2rZtmzIzM7Vs2TJlZ2crNjZWZ86ccd6Xv7+/8/5vv/12+fj4qFatWqpfv778/PwkSTabTTk5OXrhhRckSQ899JBOnTpV5mvmyJEjSk9P1/Dhw511FF/P+svHmJKSolatWummm26SJOcnxcVn29SuXVs//vhjif5bLTk5Wd7e3s4k/ttvv9Vf/vIXhYWFlflayMzMdDn2vPjii1q6dKn++7//Wz4+Ptf0XSFVrWHDhi6/gyA5Odk5Hhdr3769JKl+/fo6c+aMjhw5oqSkJOeldYWFhUpLS5P0f895TEyM/vOf/0iSPvjgA0VERGjSpEnKzs5Wv379yuxR06ZNnevw9PRUUFCQNm/erH379pW6FO3MmTPO13LxbWWNZ1fq27evpkyZottvv915xoGHh4fL7fD48eMut/22bdvKZrOpQYMGys3NLXUfxZ9c//73v9d//vOfMseCTZs2OcfmXr166Z577nF+ghQQEKCjR49K+nmMlH4ei8+cOaO9e/fqrbfekiSNHDmyxH23bt1a0s9jVvH4NHPmTL333nt64403dO+99zq/o6h4vffdd59mzJihEydOqEOHDvLw8JCnp6fGjRunhISEEp9qlTVO/eEPf5Ddbpfdbne+xsePH6933nlHsbGxatGihfMstl8+npSUFN15553y9vaW9PP+fPv27brnnntcjpXFZ5VJUlhYmKSSY/ydd97p/H9eXl6ZxxkDBw7Uhg0blJGRoW7dusluL3lYduXzWFhYqPvuu09paWlatWqVJGnEiBHKz893uU8oazu48847nfvW4uOXYhUZTwMDA/XDDz/oxRdflN1u16hRo0r1w9Vr48r72L17tz755BPnfZS1/Uuut6eyXmdXPldz5szR3LlzlZaWpnr16pW4fCM7O1v/+Mc/XPbsynW0atVKXl5e8vHx0R133KFatWrplltucXk9uKtet2zZUjfffLMkOffPvxybXCmu/cyZM+rSpYtz+i/HwivVrVvXeeZb8fHCsWPHnONHy5YtVa9ePZf3dzVHjhzRgQMHnGNuUVGR0tPTde7cObVo0UKSSnyfUfFroUGDBrp48aL+/ve/a8uWLZKk6OhozZkzR2+88YbOnj2rrl27lro/c8V3mLVp08Z5KUCHDh104sQJSSXHjujoaOdYUPwauv/++/X9999Lqtxx4S/92vcSZe1zXL0nSE1NLXX/d955p4qKipSWlqZNmzbpgw8+0OrVq0vtC48cOeIcmx966CGNGDGi1H6/IvvQLl26aM6cOTp//rz27t3rfB90rX0pdsstt6hNmzZKTEzUhg0bFBYW5nIc/eU2fuzYMQUEBMhms8nLy0v33HNPqe+dKN5evL29K7yvr45jhCuVdYx8Zc+l/xtHfHx81LJlS9lsNueYU9Z+/IcffnC+/vz9/bVy5cpS713vuOMOl89VedttcV2ujkOmTJmixMRE53FcYWGhMjMzyxy7KuKGCCXKYrPZJP08UO/du1c9evRQdna2jhw5UuJUs6ZNm/7/9u48PKbrf+D4eyYj2yTaxBYhIglN1NZaQixfhiCoNUEWxBZLK1pr2pDaQm2PpZbEFjRKJUStpYSWPoqU2FJL8VDUg1IqkUgmM78/PLnfRGZIFKnf9/P6K5mZe8+5596z3HPPObdAZRYfH0+3bt2IiIhg6dKlJCQk4OzsTFpaGvCkoZqeno6TkxNOTk4sWbIEe3t7kpOTsbW15cKFC5w+fRpfX1+uXbvG/Pnzadq0KfBkQZibN28yf/587t27x549ewotMJn3v0qlonPnzkRHR9O0adNCQ5FelE6nY+nSpQQFBVGlShVycnKYMWMGTZo0UebgPXz4kC+//JIffvgBgP79+xeKp1qtVhYTmjZtmvL59evXC8zlc3NzY+bMmTg7O3Ps2DHu3LkDPFnAKTc3l+zsbC5evIirqysffvghe/bswc7OjoiICJOLbxZ3nqA5J06c4LfffqNhw4bExcUxcOBAk3HVaDTs2rVLSZdPPvmElStXFlr3I+96M2XBggX06NGDFi1asGnTJjZv3mx2u6SkJOzt7ZkyZQpXr14lISEBo9HIO++8Q2pqKi1atODkyZMAVK5cmYoVKxIXF0epUqVISkqiRo0a7N27t1A6FeWcPsvWrVvx9/dXKpbMzExat26NjY0Nly5dwsPDg9OnT/PWW289Nz20Wm2BPLdmzRqzecbd3Z1q1aqxYsUKVCoVq1evxtPTk927dxcKo0qVKly+fJns7GwsLS0ZMWIEvXv3Zu/evSQmJpKZmUn37t2Lddyv2vnz59mwYQMxMTFYWlri5uZG6dKlmT59Ovv37zeZFxwcHEyWPcnJydSvX5/hw4ezfft2VqxYUWB62OvUunVrYmNjOXXqlHJjlJiYiIODgzLM1Bx3d3caNWrE1KlTMRgMLFmyBBcXF+C/11X+Dpfbt2+TlpbG4sWLefz4MS1atKBTp04m0+jmzZsF8kZAQAATJ07k/v37fP755wXiUb58ea5cuULVqlVZtmwZbm5uZsuz/FxcXHj06BHx8fGMGjWKa9eukZ6ebvI6zMs3T1/7z8o/+dMhj7myIP86QefPn2fVqlXo9XosLCxISUmha9eunDt3jrS0NLp06cKFCxeoUKECDRo0KJBH88pAU2EDJCQkMHnyZKysrBg4cCCpqakApKWl0aBBA44fP0716tVxd3dn/fr1eHp6YjAY6N+/P4MGDQLAYDA8s5wyFe6GDRsIDw+nTJkyfP755+zZs0cJN//xVK5cmUuXLvHo0SNsbW05evSo0iDLfz2oVCqMRiN+fn74+fk98xzkZ66d4ejoyOzZs7l16xYTJ04stN3Tx2RpaUlGRgb37t3D0dGR27dv4+TkZLJOMLV91apVuXjxIllZWZQqVYqzZ8/SuXNn5fuilKdHjhyhfPnyxMXFkZqayty5c4mPjy+QHqbyRv4wOnfuTKdOnbh79y6JiYmcO3fObDls6ryay2d55yo7O5tdu3Yxd+5cZYrgjRs3qFSpEmC+Hn06vOfls2edq7z6JisrC0tLS06dOoW7u/tzO4Pzxx2gQ4cOyho0xQkfUNoFvr6+/P7778qUmeJyd3fHx8eHSZMmkZuby+LFi5Xr99q1a7i4uBAbG0v16tVNxiU0NJTQ0FDgyc3dnj17mDdvnpKXPvjgA6ysrLhz5w6urq78+uuvSpl+6dIlsrKy0Gg0nDp1iqCgIJKTk0lLS+O9997j+PHjVKtWDXd3d9auXUtubi5qtZpffvmFnj17cvLkyQJ5OH/b9GV43r2ESqXCYDCYrZdN3ROMHj3aZBwDAgKYPXs21apVo3Tp0ibrQi8vrwJl886dOwvV+76+vs+tQ9VqNX5+fkyaNAlfX98C0yGLky759ezZkzVr1pCVlYWHhwceHh7PLUc9PDxISkqiX79+5OTkkJqaSrdu3bC0tFTyfd6D1OLU9T4+PkoYr6qNkJ+5NrKDg0ORyxxz9fjly5dJTU3Fy8uL06dPA4XvXY8cOWI2fc3dA+e/tp++RuvWrYuTkxNDhw4lKyuLmJgY7OzsTJZdeeXu8/xPd0rk6dmzJ1FRUQQFBfH48WOGDx9udj4QPOmpnTBhAjY2NqjVaqZMmUJKSgp//vknoaGhPHz4kIkTJ2JhYcH48eMZPHgwRqMRrVbLrFmzqFevHpGRkfTu3Zvc3FwiIyOVp1F16tRhyZIlhISEoFKpcHFx4fbt2wXC9/DwYMyYMcyZM4fu3bvTsmVLtmzZ8tLSw2Zx1IsAAAoaSURBVM7OjhkzZjBhwgRlvrpOp1Mu3Lzf1KtXj169eqHRaJSRIfmVKVOGnJwcZs+ezdixY82GN2nSJCIiIpT5wdOmTeP27dvo9XrCwsK4f/8+w4YNw9HRkc6dOxMSEoKNjQ1ly5YtFOaLOnz4cIEFTR8+fEh6ejrLly/H2dmZHj164O3tbTKuVatW5eeffyYoKIjc3Fw++uijYofv5+fHrFmzWLZsGU5OTs9sOPj4+DB69GhOnDiBpaUlrq6uynyyyMhI4uLisLe3R6PR4OjoSL9+/ejTpw+5ublUqlSp0HzDPObO6dNzQc1JTExk1qxZyv82Nja0bduWsmXLMm7cOOzs7NBqtUqnRHEEBgaazTNeXl74+PgQFBREdnY2derUMbsInaOjI2FhYfTu3RuVSoVOp6N27drY2NgQGBgIPHmy87Kuq5ehbdu2XLp0iYCAAGxtbTEajYwbN46UlBSzeUGtVpssezIyMoiIiCAmJkZZq6CkaLVaYmNjmT59Ovfv3yc3NxdPT0/lJiImJkZ52va0Vq1acfToUYKDg3n06BG+vr7KE25TypUrx507dwgMDEStVjNgwAAsLS1NptHNmzcLbJvXUGvVqlWhjrzJkycTGRmJWq2mXLly9OvXj4oVKxYqI0zp0KEDW7Zswc3NjWvXrmFhYWHyOnzWtV8cRSkLPD09ad++PUFBQRgMBurXr4+vry/nzp3j7NmzhIaGkpmZydSpU4sdvqenJ8HBwWi1WipUqEDdunVJSkriwIEDJCcnYzAY+OKLL3BxcaF58+bExMSQlZXFqFGjlLJs8ODBrFmzpljlVJ06dRgyZAharRZbW1tatmzJ2rVrCx2Po6Mj4eHh9O3bF7VaTZUqVRgzZkyhN6W8//77jBs3jri4OGV0V1E8q53Rrl07Dh06RJUqVZ67H5VKRVRUFGFhYdjb25Oeno5Go+HgwYOF6gRT8srAwMBAHB0dlaf4eYpSnnp5eTFq1CjWr1+PXq83WeeZyhtfffUVAEOHDmX8+PEkJCSQnp7O8OHDcXV1LVY5bK7dkCdvREPPnj1RqVTY29vj7OysfG+uHn2Z8tI6ODiYt99+m8ePHxcaCWNK/rhbW1vTtGnTAnEvjoCAAD799FNCQkJwdnbGysrqhfbTpk2bAmVuu3btsLW1ZcqUKURERKBWq6lQoQIDBgxgxYoVz9yXtbU1dnZ2yvHpdDoqVKhAaGgoUVFRODs7F5gXr9FoCA8P5+7du3Ts2FHp+Ni/fz+7d+/GaDQyY8YMKlWqhK+vL4GBgRgMBry9vdHpdMpDmjz169cnLCyMNWvWvFBamGMuj9eqVYtZs2bh4eFR5HuC/O3nvFFf8KStOG3aNGVth6LUhbVq1SpU77/77rtFqkP9/f3x9fVVRv7+U97e3kRFRTFs2LAib6PT6Th69Ci9evUiJycHPz8/atasSY8ePYiMjGTbtm3KSKni1PX5vao2Qn7m2sgbN24sclqYq8eHDRvG2LFj2blzZ5Hb7HmKcg9sqh3i6enJhAkT6N27N+np6QQHB//jsktl/Dc9EnyDldQqxbdu3WLcuHEvvXAtaUeOHFEWbxJFs3XrVurWrYurqyuJiYkcP368xJ6CCyH+/1i4cCFly5Yt0tuXiiP/QmWv06s6ntdl6dKl9O/fH0tLS8aMGUOzZs3+FW8mEQXp9XqWL1/OsGHDMBqNhISEMHLkyGK9heGfOn78OI8ePaJZs2ZcuXKFQYMGKdMo3gR5C3ivX7++wOdjxoyhe/fuhab6CSHeXDJS4g32/fffs3Dhwpf6Okfx5qpYsSIjR45URvBMnz69pKMkhBDiJdNqtcqTqEqVKtGhQ4eSjpIwQaPRkJmZSbdu3ShVqhR16tQp9NaaV83FxYVRo0axaNEi9Hr9M4eXCyFESZKREkIIIYQQQgghhCgRL2dFQCGEEEIIIYQQQohikk4JIYQQQgghhBBClAjplBBCCCGEEEIIIUSJkE4JIYQQQryRNmzYwPbt2wFYsGABycnJJRwjIYQQQhSXvH1DCCGEEG+k1NRUvL29Afj4449LODZCCCGEeBHSKSGEEEKIYomNjWXr1q1YWFjQtGlTxo4dS3x8POvXr8fCwgKdTsfYsWO5ceMGn332Gffu3cPa2pro6Gjs7Ozo27cv+/btA2DhwoUAhIeH07hxY3Q6HWfOnEGr1TJnzhwqV67Md999x6pVq8jKyuLx48dER0eTk5PDvn37OHz4MOXKlWPHjh14e3vTvXt3Nm3axKpVq1CpVNSsWZOoqCi0Wi3NmjWjXbt2HDt2DAsLC+bPn4+Li0tJJqUQQgjxP0+mbwghhBCiyH788Uf27dtHUlISmzdv5urVq8TExLBu3To2btzI1q1bSUtL48yZM0yePJl27dqxfft2wsPDiYmJeea+//rrL7y9vdm2bRsdO3YkOjoag8HAN998o3SEhIWFsXLlSpo0aUKrVq0YMWIEzZs3V/Zx/vx5YmNjiY+PZ9u2bdjY2LBo0SIA7ty5g4+PD99++y0NGzbk66+/fqVpJYQQQojnk04JIYQQQhTZ4cOH6dixI9bW1mg0Gvz9/Vm0aBE6nQ57e3s0Gg2rV6+mVq1apKSk0KVLFwBatGjBggULnrlvKysrunbtCkC3bt04cuQIarWaxYsX89NPP7FgwQI2b95MRkaG2X2kpKSg0+lwcHAAoFevXhw+fFj5Pq8Do3r16jx48OAfpYUQQggh/jnplBBCCCFEkRkMhkKfGY3GAv/funWLv//+G41GU+A3Fy9eRKVSFfi9Xq9X/lar1ahUKiUcCwsLMjIy8Pf35/r16zRs2JA+ffoUK35Go7FAGFZWVgCF4iGEEEKIkiGdEkIIIYQossaNG7Njxw6ysrLQ6/Vs2rSJiIgIDhw4QEZGBnq9ntGjR3PmzBkaNGjAjh07ADh06BBRUVGULl2aBw8ecO/ePbKzszl48KCy78zMTGWtiaSkJP7zn/9w5coV1Go1Q4cOpXHjxhw4cIDc3FwALCwslL/zeHt7s2/fPu7fvw9AQkICjRo1eh1JI4QQQogXIAtdCiGEEKLIdDodZ8+exd/fH71eT/Pmzenbty9WVlYEBgZiMBho06YNTZo0wc3NjQkTJrBu3TpsbGyIjo7G3t6egQMHEhAQgJOTE7Vr1y6w/127djFv3jzKly/PzJkzcXBwoEaNGrRv3x5ra2saNmzIH3/8AUCTJk2YO3cu9vb2yvZeXl4MGTKEPn36kJOTQ82aNZk8efJrTSMhhBBCFJ3KKGMXhRBCCPEv4Onpyfnz50s6GkIIIYR4jWT6hhBCCCGEEEIIIUqEjJQQQgghhBBCCCFEiZCREkIIIYQQQgghhCgR0ikhhBBCCCGEEEKIEiGdEkIIIYQQQgghhCgR0ikhhBBCCCGEEEKIEiGdEkIIIYQQQgghhCgR0ikhhBBCCCGEEEKIEvF/nFDMYSVynqUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Expected result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce3ccfe208>"
      ]
     },
     "execution_count": 60,
     "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(data=df, x='occupation')   # THIS IS UNORDERED\n",
    "sns.countplot(data=df, x='occupation', order=df['occupation'].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Count per `occupation`\n",
    "\n",
    "Using grouping and aggregation functions, count how many people are per each occupation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": 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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adm-clerical</th>\n",
       "      <td>3721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armed-Forces</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Craft-repair</th>\n",
       "      <td>4030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exec-managerial</th>\n",
       "      <td>3992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Farming-fishing</th>\n",
       "      <td>989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Handlers-cleaners</th>\n",
       "      <td>1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machine-op-inspct</th>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other-service</th>\n",
       "      <td>3212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Priv-house-serv</th>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof-specialty</th>\n",
       "      <td>4038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protective-serv</th>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>3584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tech-support</th>\n",
       "      <td>912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transport-moving</th>\n",
       "      <td>1572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count\n",
       "occupation              \n",
       "Adm-clerical        3721\n",
       "Armed-Forces           9\n",
       "Craft-repair        4030\n",
       "Exec-managerial     3992\n",
       "Farming-fishing      989\n",
       "Handlers-cleaners   1350\n",
       "Machine-op-inspct   1966\n",
       "Other-service       3212\n",
       "Priv-house-serv      143\n",
       "Prof-specialty      4038\n",
       "Protective-serv      644\n",
       "Sales               3584\n",
       "Tech-support         912\n",
       "Transport-moving    1572"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expected output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adm-clerical</th>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "      <td>3721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armed-Forces</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Craft-repair</th>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "      <td>4030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exec-managerial</th>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "      <td>3992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Farming-fishing</th>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "      <td>989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Handlers-cleaners</th>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "      <td>1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machine-op-inspct</th>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other-service</th>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "      <td>3212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Priv-house-serv</th>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof-specialty</th>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "      <td>4038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protective-serv</th>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "      <td>3584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tech-support</th>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transport-moving</th>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    age  workclass  fnlwgt  education  education-num  \\\n",
       "occupation                                                             \n",
       "Adm-clerical       3721       3721    3721       3721           3721   \n",
       "Armed-Forces          9          9       9          9              9   \n",
       "Craft-repair       4030       4030    4030       4030           4030   \n",
       "Exec-managerial    3992       3992    3992       3992           3992   \n",
       "Farming-fishing     989        989     989        989            989   \n",
       "Handlers-cleaners  1350       1350    1350       1350           1350   \n",
       "Machine-op-inspct  1966       1966    1966       1966           1966   \n",
       "Other-service      3212       3212    3212       3212           3212   \n",
       "Priv-house-serv     143        143     143        143            143   \n",
       "Prof-specialty     4038       4038    4038       4038           4038   \n",
       "Protective-serv     644        644     644        644            644   \n",
       "Sales              3584       3584    3584       3584           3584   \n",
       "Tech-support        912        912     912        912            912   \n",
       "Transport-moving   1572       1572    1572       1572           1572   \n",
       "\n",
       "                   marital-status  relationship  race   sex  capital-gain  \\\n",
       "occupation                                                                  \n",
       "Adm-clerical                 3721          3721  3721  3721          3721   \n",
       "Armed-Forces                    9             9     9     9             9   \n",
       "Craft-repair                 4030          4030  4030  4030          4030   \n",
       "Exec-managerial              3992          3992  3992  3992          3992   \n",
       "Farming-fishing               989           989   989   989           989   \n",
       "Handlers-cleaners            1350          1350  1350  1350          1350   \n",
       "Machine-op-inspct            1966          1966  1966  1966          1966   \n",
       "Other-service                3212          3212  3212  3212          3212   \n",
       "Priv-house-serv               143           143   143   143           143   \n",
       "Prof-specialty               4038          4038  4038  4038          4038   \n",
       "Protective-serv               644           644   644   644           644   \n",
       "Sales                        3584          3584  3584  3584          3584   \n",
       "Tech-support                  912           912   912   912           912   \n",
       "Transport-moving             1572          1572  1572  1572          1572   \n",
       "\n",
       "                   capital-loss  hours-per-week  native-country  income  \\\n",
       "occupation                                                                \n",
       "Adm-clerical               3721            3721            3721    3721   \n",
       "Armed-Forces                  9               9               9       9   \n",
       "Craft-repair               4030            4030            4030    4030   \n",
       "Exec-managerial            3992            3992            3992    3992   \n",
       "Farming-fishing             989             989             989     989   \n",
       "Handlers-cleaners          1350            1350            1350    1350   \n",
       "Machine-op-inspct          1966            1966            1966    1966   \n",
       "Other-service              3212            3212            3212    3212   \n",
       "Priv-house-serv             143             143             143     143   \n",
       "Prof-specialty             4038            4038            4038    4038   \n",
       "Protective-serv             644             644             644     644   \n",
       "Sales                      3584            3584            3584    3584   \n",
       "Tech-support                912             912             912     912   \n",
       "Transport-moving           1572            1572            1572    1572   \n",
       "\n",
       "                   <=50K  >50K  \n",
       "occupation                      \n",
       "Adm-clerical        3721  3721  \n",
       "Armed-Forces           9     9  \n",
       "Craft-repair        4030  4030  \n",
       "Exec-managerial     3992  3992  \n",
       "Farming-fishing      989   989  \n",
       "Handlers-cleaners   1350  1350  \n",
       "Machine-op-inspct   1966  1966  \n",
       "Other-service       3212  3212  \n",
       "Priv-house-serv      143   143  \n",
       "Prof-specialty      4038  4038  \n",
       "Protective-serv      644   644  \n",
       "Sales               3584  3584  \n",
       "Tech-support         912   912  \n",
       "Transport-moving    1572  1572  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# easy method, group by occupation\n",
    "df.groupby('occupation').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation\n",
       "Adm-clerical         3721\n",
       "Armed-Forces            9\n",
       "Craft-repair         4030\n",
       "Exec-managerial      3992\n",
       "Farming-fishing       989\n",
       "Handlers-cleaners    1350\n",
       "Machine-op-inspct    1966\n",
       "Other-service        3212\n",
       "Priv-house-serv       143\n",
       "Prof-specialty       4038\n",
       "Protective-serv       644\n",
       "Sales                3584\n",
       "Tech-support          912\n",
       "Transport-moving     1572\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DISPLAY ONE COLUMN WITH EASY METHOD\n",
    "df.groupby('occupation')['age'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>occupation</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adm-clerical</th>\n",
       "      <td>3721</td>\n",
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       "    <tr>\n",
       "      <th>Armed-Forces</th>\n",
       "      <td>9</td>\n",
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       "      <th>Craft-repair</th>\n",
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       "    <tr>\n",
       "      <th>Exec-managerial</th>\n",
       "      <td>3992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Farming-fishing</th>\n",
       "      <td>989</td>\n",
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       "      <th>Handlers-cleaners</th>\n",
       "      <td>1350</td>\n",
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       "      <th>Machine-op-inspct</th>\n",
       "      <td>1966</td>\n",
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       "      <td>3212</td>\n",
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       "      <th>Priv-house-serv</th>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prof-specialty</th>\n",
       "      <td>4038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protective-serv</th>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>3584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tech-support</th>\n",
       "      <td>912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transport-moving</th>\n",
       "      <td>1572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count\n",
       "occupation              \n",
       "Adm-clerical        3721\n",
       "Armed-Forces           9\n",
       "Craft-repair        4030\n",
       "Exec-managerial     3992\n",
       "Farming-fishing      989\n",
       "Handlers-cleaners   1350\n",
       "Machine-op-inspct   1966\n",
       "Other-service       3212\n",
       "Priv-house-serv      143\n",
       "Prof-specialty      4038\n",
       "Protective-serv      644\n",
       "Sales               3584\n",
       "Tech-support         912\n",
       "Transport-moving    1572"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# really need an explanation for this one\n",
    "df.groupby('occupation').size().reset_index(name='count').set_index('occupation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Display capital gains per occupation\n",
    "\n",
    "Capital gain is defined as _\"income from investment sources, apart from wages/salary\"_, is basically people generating profits from investments out of their regular job. Show the **average** capital gain per occupation. It should look something like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110a3b198>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Expected result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce3cc2eac8>"
      ]
     },
     "execution_count": 64,
     "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": [
    "# AVERAGE CAPITAL GAIN PER OCCUPATION\n",
    "sns.barplot(data=df, x='occupation', y='capital-gain') # NO ESTIMATOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce3ccebda0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=df, x='occupation', y='capital-gain', estimator=np.mean) # what is the difference with no estimator?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Use grouping to obtain the same results as the previous plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using grouping functions, generate the same results as the plot above. Expected output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation\n",
       "Adm-clerical          497.097286\n",
       "Armed-Forces            0.000000\n",
       "Craft-repair          657.586849\n",
       "Exec-managerial      2185.330912\n",
       "Farming-fishing       592.707786\n",
       "Handlers-cleaners     258.140000\n",
       "Machine-op-inspct     323.825025\n",
       "Other-service         181.705479\n",
       "Priv-house-serv       291.594406\n",
       "Prof-specialty       2717.880139\n",
       "Protective-serv       713.596273\n",
       "Sales                1308.637835\n",
       "Tech-support          668.105263\n",
       "Transport-moving      498.121501\n",
       "Name: capital-gain, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expected result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation\n",
       "Adm-clerical          497.097286\n",
       "Armed-Forces            0.000000\n",
       "Craft-repair          657.586849\n",
       "Exec-managerial      2185.330912\n",
       "Farming-fishing       592.707786\n",
       "Handlers-cleaners     258.140000\n",
       "Machine-op-inspct     323.825025\n",
       "Other-service         181.705479\n",
       "Priv-house-serv       291.594406\n",
       "Prof-specialty       2717.880139\n",
       "Protective-serv       713.596273\n",
       "Sales                1308.637835\n",
       "Tech-support          668.105263\n",
       "Transport-moving      498.121501\n",
       "Name: capital-gain, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['capital-gain'].groupby(df['occupation']).aggregate(np.mean) # aggregate to get the mean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.SeriesGroupBy object at 0x7fce3cc856a0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rechecking the data\n",
    "df['capital-gain'].groupby(df['occupation']) # just an object, huh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2174\n",
       "1            0\n",
       "2            0\n",
       "3            0\n",
       "4            0\n",
       "5            0\n",
       "6            0\n",
       "7            0\n",
       "8        14084\n",
       "9         5178\n",
       "10           0\n",
       "11           0\n",
       "12           0\n",
       "13           0\n",
       "15           0\n",
       "16           0\n",
       "17           0\n",
       "18           0\n",
       "20           0\n",
       "21           0\n",
       "22           0\n",
       "23           0\n",
       "24           0\n",
       "25           0\n",
       "26           0\n",
       "29           0\n",
       "30           0\n",
       "31           0\n",
       "33           0\n",
       "34           0\n",
       "         ...  \n",
       "55           0\n",
       "57           0\n",
       "60        2407\n",
       "62           0\n",
       "64           0\n",
       "65           0\n",
       "71           0\n",
       "75           0\n",
       "79           0\n",
       "84       14344\n",
       "86           0\n",
       "94           0\n",
       "95           0\n",
       "107          0\n",
       "145          0\n",
       "167          0\n",
       "171          0\n",
       "195          0\n",
       "227       1409\n",
       "255          0\n",
       "442          0\n",
       "536          0\n",
       "622          0\n",
       "929          0\n",
       "1038         0\n",
       "1300         0\n",
       "2065         0\n",
       "14613        0\n",
       "16111        0\n",
       "18034        0\n",
       "Name: capital-gain, Length: 70, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['capital-gain'].groupby(df['occupation']).head() # not quite what I was thinking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Bar plot: average capital gain per race and group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a bar plot that shows in that has `race` as the X axis, a `sex` hue and displays the average capital gain for each group. Expected output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110324dd8>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Expected output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fce3db45dd8>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pXsEdvx/FAjPrQdQspT4LEZFWItWzoU6uo2wD8I67b2jyqEREJKek2gz1E2AI8HaY/hrwFtDXzL7n7vPqXVJERJq9VM+GWgoc5u6HhKu6hwHvA0cDk9MVnIiI5IZUk8Ugd389PuHubwBfc/f3qftueCIi0oKkmiw2mtlZ8YnwfFOYjDV5VCIiklNS7bM4H3jMzIrC9LvAGDPbBZhQ1wJm1g+YCfQiSijT3X1qOJvqSWBPYBlwhruXmFkeMBU4GdgInBdqMJjZWKIryAEmu/uMRr1LERHZKameOvs+MMTMuobpxNNmi+tZbCtwjbu/EZZ73cyKgfOAF9z9NjO7FrgW+BFwEjAg/A0D7gOGheQyiaiDPRbWM9fdSxr3VkVEZEc1atTZkCRuSHHelfGaQVjufaAvMBKI1wxmAKPC85HATHePufsrwK5m1hv4JlDs7utDgigmGp9KREQyJNVmqERHN3YBM9sT+DrwKtDL3VeGl1YRNVNBlEg+TVhseSirr7xeBQWdyc/XgLiSHu3bV9WY7tmzC927d81SNJIrWvrvYkeSRaPOfjKzLsBTwJXu/oWZVb/m7jEza/IO8pIS3e1V0qe0tKzG9Lp1ZWzZolvDtHYt4XdRWFh/ctuRd3JuqjOaWTuiRPG4u/8uFK8OzUuExzWhfAXQL2HxPUJZfeUiIpIhDdYszGxgHcVV8XJ3f6+BZfOAh4H3E8aWApgLjAVuC4/PJJSPM7NZRB3cG9x9pZktBG41s4Iw3wnAdUnfmYiINJlkzVDPNvBaDPhqA68fQVQLedvM3gxlE4mSxGwzuxD4BDgjvDaf6LTZpUSnzp4P4O7rzexmYHGY7yZ3X58kbhERaUINJgt332tHV+zui6i/f+PYOuaPEd2Br651FQFFdb0mIiLp16gObjPbHegYn3b3fzd5RCIiknNSHaL8GKJrInoBlUB7YB2we/pCExGRXJHq2VB3EDUdvQt0Bi4BpqcrKBERyS0pnzrr7v8E2oUrrB9CV1GLiLQaqfZZVITHFWb2baIBAHukJSIREck5qSaLqeE6h+uBJ4DuwFVpi0pERHJKqsniWXf/guhah30AzKxb2qISEZGckmqfxV9SLBMRkRYo2XAf+USnybYxs05su8iuO9FZUSIi0gokq1n8GCgDvgZ8GZ6XEd2b4vH0hiYiIrki2XAfNwI3mtkv3X1chmISEZEck1KfhRKFiEjrlqzP4gV3P9bM1hKNMhuXB8TcXcN9iIi0AslOnf1ueByS7kBEsumKO+amPG/V1vIa0xN/uYA2+R3rmXt7Uyd8J+V5RXJFg81Q8Xtlu/snwGdA1/C3IpSJiEgrkFKfhZmNAD4iukXq74CPzOzwdAYmIiK5I9WL8qYB33V3c/d9gXOA+9IXloiI5JLGjDr714Tnf0tPOCIikotSTRbFZnZOfMLMzgYWpickERHJNakOJDgWuNrMHgrTHYB1ZnY+OoVWRFoonSW3TarJotGnzppZEXAKsMbdDwxlNwAXAWvDbBPdfX547TrgQqLbto5394Wh/ERgKtAWeMjdb2tsLCIisnNSShY7eJrso8AvgZm1yu9x9zsTC8xsIDAaOADoAzxvZvuGl6cBxwPLgcVmNtfd39uBeEREZAellCzMrB9wOzAIqK5XuftX61vG3V80sz1TjGMkMMvdNwMfm9lSYGh4bam7fxTimBXmzWiyKCqaznPPzeeEE07mggsuzuSmRURyQqrNUEXALOBgotNmLwU+3MFtjjOzMcAS4Bp3LwH6Aq8kzLM8lAF8Wqt8WLINFBR0Jj+/7Q6GV9OmTZsoLv4jAM8/v4Dx4y+jU6dOTbJuaZ0KC7tmOwTJQbn+u0g1Wezm7g+b2ZXu/rKZvQq8DNzYyO3dB9xMNM7UzcBdwAWNXEdSJSUbm2xdpaVfEItFw2JVVVWxcuV6unbVTQJlx61dW5rtECQH5cLvoqGElWqy2BIey8ysP7AaKGxsIO6+Ov7czB4E5oXJFUC/hFn3CGU0UC4iIhmSarJ40cx6AL8CXgc2A79t7MbMrHd8vCngVOCd8Hwu8Bszu5uog3sA8BrR6LYDzGwvoiQxGji7sdsVEZGdk2qyWAhUuvtjZvZXoD/R7VbrZWZPAEcBu5nZcmAScJSZHUzUDLUMuATA3d81s9lEHddbgcvdvTKsZ1zYflugyN3fbdQ7FBGRnZZqsrgDOATA3f8ddv5L4mV1cfez6ih+uIH5bwFuqaN8PjA/xThFRCQNUk0Wee5effMjd68ys6Y53SjDGnNFJuzcVZm5fkWmiEiqUh0bqtTMqk9ZDc+/TE9IIiKSa1KtWfwQeNrM4v0FA4H/Tk9IIpJIF4VKLkipZuHuLxMliLvD3wHu/krDS4nIziov33ZRaHHxAsrLN2U5ImmtUq1ZEK60VkezSAZVVFRUXxQai1VRUVFBx44aQUAyL+WbH4mISOulZCEiIkkpWSSTl3iGcF6taRGR1kHJIok2bdvRqXB/ADoV7kebtu2yHJGISOal3MHdmnXrP5xu/YdnOwwRkaxRzUJEdkpR0XRGjx5FUdH0FrUtqUnJQkR2WCavA9E1J9mlZCEiO6yu60BawrZke0oWIiKSlJKFiIgkpWQhIiJJKVmIiEhSShYiIpKUkoWIiCSVtiu4zawIOAVY4+4HhrIewJPAnsAy4Ax3LzGzPGAqcDKwETjP3d8Iy4wFrg+rnezuM9IVs4iI1C2dNYtHgRNrlV0LvODuA4AXwjTAScCA8HcxcB9UJ5dJwDBgKDDJzArSGLOIiNQhbcnC3V8E1tcqHgnEawYzgFEJ5TPdPRbuwLermfUGvgkUu/v6cPOlYrZPQJIhGmpBpPXK9ECCvdx9ZXi+CugVnvcFPk2Yb3koq6+8QQUFncnPz/5Q4oWFXbMdQpPZtGnbUAvPP7+A8eMvo1Mn3bFtRzTmd9G+fVWN6Z49u9C9e+78rjIZX65/Fjsr1/cXWRt11t1jZhZLx7pLSjamY7WNtnZtabZDaDKlpV9UD7VQVVXFypXr6dq1W5ajap4a87soLS2rMb1uXRlbtuTOeSmZjC/XP4udlQv7i4YSVqY/6dWheYnwuCaUrwD6Jcy3Ryirr1xERDIo08liLjA2PB8LPJNQPsbM8szsMGBDaK5aCJxgZgWhY/uEUCYiIhmUzlNnnwCOAnYzs+VEZzXdBsw2swuBT4AzwuzziU6bXUp06uz5AO6+3sxuBhaH+W5y99qd5iIikmZpSxbuflY9Lx1bx7wx4PJ61lMEFDVhaCIi0kgtp3dIJFPyEs+0y6s1LdIyKVmINFKbtu3oVLg/AJ0K96NN23ZZjkgk/bJ26qxIc9at/3C69R+e7TBEMkY1CxERSUrJQkREklKykJykcahEcouSheSc8vJt41AVFy+gvHxTliMSESULyTkVFRXV41DFYlVUVFRkOSIRUbIQEZGklCxERCQpXWfRil1xx9yU563aWl5jeuIvF9Amv2PKy0+d8J2U5xWR3KOaRQ7RGUAikqtUs8gRtc8AOvvsc+nYUXeik8xTjVPqoppFjtAZQCKSy5QsREQkKSULERFJSn0WIpI1E+Zdn/K8lZu31pie9NyttO2Q+i7sjlMmpzyvbE/JIo30jyAiLYWaoUREJKms1CzMbBlQClQCW919iJn1AJ4E9gSWAWe4e4mZ5QFTgZOBjcB57v5GFsIWaRKqcUpzlM2axdHufrC7DwnT1wIvuPsA4IUwDXASMCD8XQzcl/FIRURauVxqhhoJzAjPZwCjEspnunvM3V8BdjWz3tkIUESktcpWB3cMeM7MYsAD7j4d6OXuK8Prq4Be4Xlf4NOEZZeHspXUo6CgM/n5bZs+6hxWWNg12yE0qDHxtW9fVWO6Z88udO+e2+8vV+X67yKTcv2zaGx89957L8888wwjR45k/PjxaYpqm2wlixHuvsLMdgeKzeyDxBfdPRYSyQ4pKdm40wFmWl6bvISJWtMpWLu2tIkjqiUvMfnm1ZpO7rxHrkh53trt9OOemKh2+h2U9t9FM5Lrn0Vj4isv38TcudGwLHPn/oFTTx3dJMMDNZSwstIM5e4rwuMa4PfAUGB1vHkpPK4Js68A+iUsvkcoa1HatGtLl317ANBlQA/atMutmlGbtu3oVLg/AJ0K96NN23ZZjkik9crG8EAZr1mY2S5AG3cvDc9PAG4C5gJjgdvC4zNhkbnAODObBQwDNiQ0V7UoBUP7UDC0T7bDqFe3/sPp1n94tsMQkSzIRjNUL+D3Zhbf/m/cfYGZLQZmm9mFwCfAGWH++USnzS4lOnX2/MyHLCLSumU8Wbj7R8CgOsrXAcfWUR4DLs9AaCIiUo9cOnVWRERylJKFiIgkpWQhIiJJKVmIiEhSShYisuN28mJNaT6ULERkh+lizdZDNz8SkZ2iizVbB9UsREQkKSULyTk7O6iiiDQ9JQvJObk+qKJIa6Q+C8lJuT6ookhTy/Xb7apmISIiSSlZiEizoL6s7FKyEJFmQX1Z2aU+CxFpNtSXlT2qWYiISFJKFiI5TO30zUgLHydLyUIkh6mdvvlo6eNkqc9CJMepnb75aMnjZKlmISIiSTWbmoWZnQhMBdoCD7n7bVkOSUSk1WgWNQszawtMA04CBgJnmdnA7EYlItJ6NItkAQwFlrr7R+6+BZgFjMxyTCIirUZeLBbLdgxJmdlpwInu/r0wfS4wzN3HZTcyEZHWobnULEREJIuaS7JYAfRLmN4jlImISAY0l7OhFgMDzGwvoiQxGjg7uyGJiLQezaJm4e5bgXHAQuB9YLa7v5vdqEREWo9m0cEtIiLZ1SxqFiIikl1KFiIiklRz6eDOKjO7B/jE3X8ephcCnyZc93EXUcf7Me5+Sh3LPwTc7e7vmdlEd781g+E3OTOrBN4G8oBKYJy7v2RmewLz3P3AHVjnX4D/dfclO7DsKOD3wP7u/kED880Hznb3zxu7jTrWtQwoBWLAKmCMu6/ayXU+SvT5zann9RuAMne/M6Espffe2G015XLhsxri7v8xs5fc/fDGbLOB9cZ/h3Gz6hsGqL64E/83myKmhPXuQTTqxECig/J5wIQw3cfd54f5bqDWd5qrVLNIzf8BhwOYWRtgN+CAhNcPB9rXt7C7fy/hxzgxXUFm0CZ3P9jdBwHXAVOyHM9ZwKLwWC93P7kpEkWCo939IGAJ2fteU3rvO8LM8ut6vjOaKlEE8d9h/K/R48XV+t9sEmaWB/wOeNrdBwD7Al2AW4CDgZObcFsZG7NeNYvUvATcE54fALwD9DazAmAjsD8wCTjRzOYABwKvA99191j8qBk4DehkZm8C77r7OWb2XWA8UbJ5FbjM3Ssz99Z2WjegpHZhqGU8BuwSisa5+0vhtR8B3wWqgD+6+7UJy7UBioDl7n59so2bWRdgBHA08Adgkpn1Bp4MseUDl7r732od4T5NdO1OR2Cqu08P6ysjGrDyFGATMNLdVycJ40Wi7xAzuw84FOgEzHH3SaH80LDeXYDNwLHuXtrA+7oN+A6wFXjO3f+31usXAd8HDgL+RJQsJpnZAqLfX2egK9ER7RzgCmBvYAPwUVjN/4bvojOwhej76A68CfQG9jGzJ8Ln+wRwV60YlgEzgG8D7YDT3f0DM+sZ5u8LvExUA40vU+buXcL39gxQEJa93t2fCb+bPxIlwMOJauwj3X1TfZ/VDnx2NxN99xcCLxBqtOG7v49oZ76S6ADgdqA/cKW7zzWzjmGeIWH9V7v7n2uFcAxQ7u6PALh7pZldBXwCVAB5ZjaCbQdZA8M+oj/wc3e/N8RZ574hxPkAcBxwefis0k41ixS4+2fAVjPrT/QDfpnoyxtO9KN5m+if7evAlURVza8CR9Raz7VsOxo6x8z2B84EjnD3g4madM7JzLvaKZ3M7E0z+wB4CLi5jnnWAMe7+yFE7zH+D3AS0bhew0LN5PaEZfKBx4F/pZIogpHAAnf/J7DOzAYTXYOzMHymg4h2frVd4O6Dib6/8WEHB9HO/JUQ24vARSnEcArbmkN+7O5DiHbi3zCzg8ysPVHyuiKs9ziiRFSnEMupwAGh5jK5jtl+B9wNzCS6Dik/vHeA3YkSxhCiHfm+wG3AWqKEOgY4kiiRHB5i2SV8Hn8HjgrNEs/dAAAG1UlEQVQxvg+0d/ch7l4jUST4T/iO7yM6IILowGmRux9A1ETWv47lyoFTw7JHA3eFI3KAAcC0sPznwP/Us+347zD+d2ayz87M7gAKgfPrOCjbBfhT2G5pWPb4sL6bwjyXAzF3/xpRgp4REkiiA4gOFqu5+xfAsrDOJ8M+4Mnw8n7AN4nGwJtkZu2S7Bt2AV5190HunpFEAapZNMZLRP9YhxP9k/YNzzcQNVMBvObuywFC7WFPGs76xwKDgcVmBtHR6Jo0xN7UNoUfMGY2HJhpZrX7KdoBvzSz+A9931B+HPCIu28EcPf1Ccs8QHQNzS2NiOUsoiN2iAaYPAuYCxSZWTuipoC6ksV4Mzs1PO9HtINaR5T054Xy14l2FvX5c2g3fwuIJ7czzOxiov+t3kQHDjFgpbsvhuodR0M2EO1MHzazeQnxJDqQKAHH17WCbU1RHg5wPgu/q+eA04k+l33d/TMzqyI6sv5ueO+VZvYW0Av43N03hWXjO7T6/C48vg78d3h+ZPy5uz9rZtvVPIlqG7ea2ZFENZq+YdsAHyd8Z68T/R/Vpfp3GBeay+r77H5CtJO9uJ71bQEWhOdvA5vdvcLM3k6IYQTwi/DePjCzT4h+22/Vs85UPOvum4HNZraG6HNoaN9QCTy1E9vbIUoWqYv3W3yNqBnqU+Aaon/WR8I8mxPmryT555sHzHD365o21Mxx95fNbDeio7VEVwGriY7s2xD9AyfzEnC0md3l7knnN7MeRFX+r5lZjOheJzGijsQjgW8Bj5rZ3e4+M2G5o4iS1nB33xiaAOJHhxXuHr/4qJLoiL0t244U57r7T8Pzo939Pwnr3Yvo6PpQdy8Jnaq1jzoT43+EqDb6mbtXt2O7+1YzG0q0wziN6ILUY2otPpOo+aiM6Ejz60RH8O9R83eYlzAdY9tvMo+omeUdYLq7Dw8x3RDWGXeNmU2tHWOC+LpT+b0nOofoNzM47JCXse2zqv1/1MnM+hHVigDud/f761ppks9uMTDYzHrUOkiJS/zuq+JxuHtVI/ts3gvbrmZm3Yi+n611zF/XfqOhfUN5Npqq1QyVupeImhvWu3tl+LHtStQU9VIj1lMRjnghai89zcx2h2jnZ2Zfacqg083M9iPaSa+r9VJ3oqPpKuDcMA9AMXC+mXUOy/dIWOZhYD4wO8V/ztOAx9z9K+6+p7v3Az4mShSr3f1BomayQ+qIrSQkiv2AwxraSPi+452oP21g1m7Al8AGM+tFdP8VACfq4zo0vOeuZpbv7ueHddbYCYf2/O4enTFzFVHCra0HMBvYh6jp6LfhvfeqY16ImtSODuvvTbQzOinEVmhmY8J8bWqt46a6YkziRcJwPKHZsaCOeboDa0KiOBpo8Hfv7p8mfAd1JoqwvYY+uwVEzXHPmlnXRryfRH8jNAeZ2b5ECcBrzfMC0Dn+mYaDjbuAR4kOoFLZds7tG5QsUvc20VlQr9Qq25B4dJmC6cBbZvZ4OAvjeuC50ARQTNR0keuq24qJminG1nGk8ytgrJn9g6hN9ksAd19A1ByyJCxfo/PR3e8m2vk9Fjq7G3IWUZt4oqeI/in/YWZ/J2r3nVprngVENYb3iXYer9AE3P0fRLF/APyG0Dzp0T1YzgR+ET6PYhqocRA6psNvYhFwdR3zfEbUzv1/YXsQvfe96lnn74maqo4iqpX8lajDewlR4rgtxPZ96m/2SdWNwJFm9i5Rc9S/65jncWBIaOIZk/AeGqN2n8VtJPns3P23wIPAXDPrtAPb/BXQJsT9JHBeaEJK3EaMqJ/jdDP7F/BPopr1RODPRB3ab5rZmfVtJBf3DRruQ0REklLNQkREklKyEBGRpJQsREQkKSULERFJSslCRESSUrIQEZGklCxEmlgjr/YVaRZ0nYVIEwjDjdxINMTIAqKrq39FNBRHR6IhNeL3Q+lONIrxoUTDSvzN3ceFAQdvAb4BdCAab+hSdy9DJMtUsxBpOpvc/VB3/wnRCKPHhVFVhwIXh5FEAX5OdEX7oDAK7Q2h/IdEIwIMDeWfEd0vRCTrVF0WaTozEp53Bu4zs0FEtYc+ROMUvU80xtjgMG4WCcPFfAfoZmbxQeg6AP/IROAiyShZiDSdxOaiW4lut3peGAn1ORoeDwqiMZouc/c/pStAkR2lZiiR9NiV6D7tW8O9Pv4r4bV5wIT4zX7CEO8QDbB4dXyAuzA67f6I5AAlC5H0mAxcFEYMvYFo2O64q4hGR30njPQaH/b8NqJmp8UJo6YqWUhO0NlQIiKSlGoWIiKSlJKFiIgkpWQhIiJJKVmIiEhSShYiIpKUkoWIiCSlZCEiIkn9f77LmmI1orR3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hue gives you that categorical sex divider in the plotting\n",
    "sns.barplot(data=df, x='race', y='capital-gain', hue='sex', estimator=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Use grouping to obtain the same results as the previous plot\n",
    "\n",
    "Use group functions to generate the values plotted above. You're basically grouping by `race` and `sex` and computing the average capital gain per group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race                sex   \n",
       "Amer-Indian-Eskimo  Female     605.682243\n",
       "                    Male       692.932961\n",
       "Asian-Pac-Islander  Female     564.204082\n",
       "                    Male      1666.455907\n",
       "Black               Female     484.871337\n",
       "                    Male       722.966855\n",
       "Other               Female     223.873563\n",
       "                    Male      1566.208333\n",
       "White               Female     588.850285\n",
       "                    Male      1385.042466\n",
       "Name: capital-gain, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Expected output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "race                sex   \n",
       "Amer-Indian-Eskimo  Female     605.682243\n",
       "                    Male       692.932961\n",
       "Asian-Pac-Islander  Female     564.204082\n",
       "                    Male      1666.455907\n",
       "Black               Female     484.871337\n",
       "                    Male       722.966855\n",
       "Other               Female     223.873563\n",
       "                    Male      1566.208333\n",
       "White               Female     588.850285\n",
       "                    Male      1385.042466\n",
       "Name: capital-gain, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at capital gain data and group by race and sex to get a numerical representation \n",
    "df['capital-gain'].groupby([df['race'], df['sex']]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![separator2](https://user-images.githubusercontent.com/7065401/39119518-59fa51ce-46ec-11e8-8503-5f8136558f2b.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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