new_cust.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get all packages needed\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "import math as ma\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmpgxls = pd.ExcelFile('KPMG.xlsx')\n",
    "df = pd.read_excel(kmpgxls, sheetname=\"NewCustomerList\")\n",
    "#list(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fname</th>\n",
       "      <th>lname</th>\n",
       "      <th>gender</th>\n",
       "      <th>3y_bike_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>JT</th>\n",
       "      <th>Category</th>\n",
       "      <th>wealth_segement</th>\n",
       "      <th>D_Indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>...</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>prop_val</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "      <th>Unnamed: 19</th>\n",
       "      <th>Unnamed: 20</th>\n",
       "      <th>rank</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chickie</td>\n",
       "      <td>Brister</td>\n",
       "      <td>Male</td>\n",
       "      <td>86</td>\n",
       "      <td>1957-07-12</td>\n",
       "      <td>General Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.7000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.743750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.71875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Morly</td>\n",
       "      <td>Genery</td>\n",
       "      <td>Male</td>\n",
       "      <td>69</td>\n",
       "      <td>1970-03-22</td>\n",
       "      <td>Structural Engineer</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.8900</td>\n",
       "      <td>1.112500</td>\n",
       "      <td>0.945625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.71875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ardelis</td>\n",
       "      <td>Forrester</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>1974-08-28 00:00:00</td>\n",
       "      <td>Senior Cost Accountant</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.0100</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.71875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lucine</td>\n",
       "      <td>Stutt</td>\n",
       "      <td>Female</td>\n",
       "      <td>64</td>\n",
       "      <td>1979-01-28</td>\n",
       "      <td>Account Representative III</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.0875</td>\n",
       "      <td>1.087500</td>\n",
       "      <td>1.087500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.70312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Melinda</td>\n",
       "      <td>Hadlee</td>\n",
       "      <td>Female</td>\n",
       "      <td>34</td>\n",
       "      <td>1965-09-21</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.5200</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.70312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Druci</td>\n",
       "      <td>Brandli</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1951-04-29</td>\n",
       "      <td>Assistant Media Planner</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.5375</td>\n",
       "      <td>0.537500</td>\n",
       "      <td>0.537500</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.67188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rutledge</td>\n",
       "      <td>Hallt</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>1976-10-06</td>\n",
       "      <td>Compensation Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.67188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nancie</td>\n",
       "      <td>Vian</td>\n",
       "      <td>Female</td>\n",
       "      <td>74</td>\n",
       "      <td>1972-12-27</td>\n",
       "      <td>Human Resources Assistant II</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.7250</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.616250</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.65625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Duff</td>\n",
       "      <td>Karlowicz</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>1972-04-28</td>\n",
       "      <td>Speech Pathologist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.2875</td>\n",
       "      <td>1.609375</td>\n",
       "      <td>1.367969</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.65625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Barthel</td>\n",
       "      <td>Docket</td>\n",
       "      <td>Male</td>\n",
       "      <td>72</td>\n",
       "      <td>1985-08-02</td>\n",
       "      <td>Accounting Assistant IV</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.0500</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.892500</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.64062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rockwell</td>\n",
       "      <td>Matson</td>\n",
       "      <td>Male</td>\n",
       "      <td>94</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>Programmer Analyst I</td>\n",
       "      <td>Retail</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.5800</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.64062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Wheeler</td>\n",
       "      <td>Winward</td>\n",
       "      <td>Male</td>\n",
       "      <td>48</td>\n",
       "      <td>1999-08-30</td>\n",
       "      <td>Environmental Specialist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.467500</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1.625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Olag</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>60</td>\n",
       "      <td>1990-05-13</td>\n",
       "      <td>Human Resources Manager</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.4300</td>\n",
       "      <td>0.537500</td>\n",
       "      <td>0.456875</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1.60938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Melba</td>\n",
       "      <td>Spellacy</td>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "      <td>1976-12-09</td>\n",
       "      <td>VP Marketing</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.8400</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.892500</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1.59375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mandie</td>\n",
       "      <td>Feares</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>1964-04-19</td>\n",
       "      <td>Clinical Specialist</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.8000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1.59375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dukie</td>\n",
       "      <td>Swire</td>\n",
       "      <td>Male</td>\n",
       "      <td>88</td>\n",
       "      <td>1954-03-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.7250</td>\n",
       "      <td>0.906250</td>\n",
       "      <td>0.906250</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1.5625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Marcelia</td>\n",
       "      <td>Monkleigh</td>\n",
       "      <td>Female</td>\n",
       "      <td>61</td>\n",
       "      <td>1993-08-22</td>\n",
       "      <td>Associate Professor</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.0750</td>\n",
       "      <td>1.075000</td>\n",
       "      <td>0.913750</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.54688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Winnifred</td>\n",
       "      <td>Beswetherick</td>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>1976-06-08 00:00:00</td>\n",
       "      <td>Actuary</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>0.956250</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.54688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Odilia</td>\n",
       "      <td>Quick</td>\n",
       "      <td>Female</td>\n",
       "      <td>65</td>\n",
       "      <td>1938-11-09</td>\n",
       "      <td>General Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.6625</td>\n",
       "      <td>0.828125</td>\n",
       "      <td>0.828125</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19</td>\n",
       "      <td>1.53125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Karly</td>\n",
       "      <td>Willavize</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>1954-08-12</td>\n",
       "      <td>Internal Auditor</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.4600</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19</td>\n",
       "      <td>1.53125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Teddie</td>\n",
       "      <td>Burchill</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>1968-12-21</td>\n",
       "      <td>Programmer I</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.0625</td>\n",
       "      <td>1.328125</td>\n",
       "      <td>1.128906</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1.51562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Gaston</td>\n",
       "      <td>Dallaghan</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1993-09-29</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.962500</td>\n",
       "      <td>0.818125</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1.51562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Otis</td>\n",
       "      <td>Ottey</td>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>1998-02-05</td>\n",
       "      <td>Quality Engineer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.0200</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>0.867000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tabbatha</td>\n",
       "      <td>Averill</td>\n",
       "      <td>Female</td>\n",
       "      <td>5</td>\n",
       "      <td>1977-12-17 00:00:00</td>\n",
       "      <td>Quality Control Specialist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brena</td>\n",
       "      <td>Schnitter</td>\n",
       "      <td>Female</td>\n",
       "      <td>78</td>\n",
       "      <td>1982-10-11</td>\n",
       "      <td>Account Executive</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.6100</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>0.518500</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.48438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Rourke</td>\n",
       "      <td>Gillbard</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>1945-08-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.5900</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.501500</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1.46875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Dyane</td>\n",
       "      <td>Burwell</td>\n",
       "      <td>Female</td>\n",
       "      <td>19</td>\n",
       "      <td>1952-06-27</td>\n",
       "      <td>Engineer II</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.1125</td>\n",
       "      <td>1.112500</td>\n",
       "      <td>0.945625</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1.46875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Claudine</td>\n",
       "      <td>Barstowk</td>\n",
       "      <td>Female</td>\n",
       "      <td>71</td>\n",
       "      <td>1966-07-20</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>Property</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.8375</td>\n",
       "      <td>1.046875</td>\n",
       "      <td>1.046875</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1.46875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Blinnie</td>\n",
       "      <td>Roze</td>\n",
       "      <td>Female</td>\n",
       "      <td>84</td>\n",
       "      <td>1960-12-10</td>\n",
       "      <td>Librarian</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.0800</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1.46875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Rhona</td>\n",
       "      <td>De Freyne</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>1960-11-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Health</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.7300</td>\n",
       "      <td>0.912500</td>\n",
       "      <td>0.912500</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1.46094</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>Mavra</td>\n",
       "      <td>Finan</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>1967-08-23</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>Property</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.0875</td>\n",
       "      <td>1.359375</td>\n",
       "      <td>1.359375</td>\n",
       "      <td>967.0</td>\n",
       "      <td>967</td>\n",
       "      <td>0.435625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>Frieda</td>\n",
       "      <td>Tavinor</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>1999-03-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.4900</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>972.0</td>\n",
       "      <td>972</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>Ellwood</td>\n",
       "      <td>Budden</td>\n",
       "      <td>Male</td>\n",
       "      <td>82</td>\n",
       "      <td>1998-06-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.2375</td>\n",
       "      <td>1.546875</td>\n",
       "      <td>1.314844</td>\n",
       "      <td>972.0</td>\n",
       "      <td>972</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>Alex</td>\n",
       "      <td>Patshull</td>\n",
       "      <td>Female</td>\n",
       "      <td>37</td>\n",
       "      <td>1966-01-02</td>\n",
       "      <td>Senior Quality Engineer</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.887500</td>\n",
       "      <td>0.754375</td>\n",
       "      <td>974.0</td>\n",
       "      <td>974</td>\n",
       "      <td>0.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>Aundrea</td>\n",
       "      <td>Outridge</td>\n",
       "      <td>Female</td>\n",
       "      <td>77</td>\n",
       "      <td>2001-01-24</td>\n",
       "      <td>Structural Engineer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>12</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.8375</td>\n",
       "      <td>1.046875</td>\n",
       "      <td>1.046875</td>\n",
       "      <td>974.0</td>\n",
       "      <td>974</td>\n",
       "      <td>0.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>Amby</td>\n",
       "      <td>Bodega</td>\n",
       "      <td>Male</td>\n",
       "      <td>63</td>\n",
       "      <td>1968-06-12</td>\n",
       "      <td>Recruiter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.6125</td>\n",
       "      <td>0.612500</td>\n",
       "      <td>0.612500</td>\n",
       "      <td>974.0</td>\n",
       "      <td>974</td>\n",
       "      <td>0.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>Esme</td>\n",
       "      <td>Pilipets</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>1967-05-06</td>\n",
       "      <td>Environmental Tech</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.2625</td>\n",
       "      <td>1.578125</td>\n",
       "      <td>1.341406</td>\n",
       "      <td>977.0</td>\n",
       "      <td>977</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>Beverly</td>\n",
       "      <td>Domnick</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1938-12-10</td>\n",
       "      <td>Structural Analysis Engineer</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.4800</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>977.0</td>\n",
       "      <td>977</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>Artemis</td>\n",
       "      <td>Swanson</td>\n",
       "      <td>Male</td>\n",
       "      <td>77</td>\n",
       "      <td>1977-02-12</td>\n",
       "      <td>Web Designer II</td>\n",
       "      <td>Argiculture</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.0500</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.892500</td>\n",
       "      <td>979.0</td>\n",
       "      <td>979</td>\n",
       "      <td>0.4165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>Daryle</td>\n",
       "      <td>Marginson</td>\n",
       "      <td>Male</td>\n",
       "      <td>93</td>\n",
       "      <td>1986-06-27</td>\n",
       "      <td>Environmental Tech</td>\n",
       "      <td>Argiculture</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>12</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.584375</td>\n",
       "      <td>979.0</td>\n",
       "      <td>979</td>\n",
       "      <td>0.4165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>Tyne</td>\n",
       "      <td>Anshell</td>\n",
       "      <td>Female</td>\n",
       "      <td>71</td>\n",
       "      <td>1992-04-08</td>\n",
       "      <td>Mechanical Systems Engineer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.6375</td>\n",
       "      <td>0.637500</td>\n",
       "      <td>0.541875</td>\n",
       "      <td>979.0</td>\n",
       "      <td>979</td>\n",
       "      <td>0.4165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>Leona</td>\n",
       "      <td>Shorrock</td>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>1951-08-23</td>\n",
       "      <td>Senior Quality Engineer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>979.0</td>\n",
       "      <td>979</td>\n",
       "      <td>0.4165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>Bertrando</td>\n",
       "      <td>Carass</td>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>1956-06-25</td>\n",
       "      <td>Sales Associate</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.578000</td>\n",
       "      <td>983.0</td>\n",
       "      <td>983</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>Augusta</td>\n",
       "      <td>Munns</td>\n",
       "      <td>Female</td>\n",
       "      <td>5</td>\n",
       "      <td>1951-09-17</td>\n",
       "      <td>Quality Control Specialist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>1.087500</td>\n",
       "      <td>0.924375</td>\n",
       "      <td>983.0</td>\n",
       "      <td>983</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>Pauline</td>\n",
       "      <td>Dallosso</td>\n",
       "      <td>U</td>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Desktop Support Technician</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>985.0</td>\n",
       "      <td>985</td>\n",
       "      <td>0.408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>Lauralee</td>\n",
       "      <td>Fudge</td>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>1987-09-21</td>\n",
       "      <td>Research Nurse</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.637500</td>\n",
       "      <td>985.0</td>\n",
       "      <td>985</td>\n",
       "      <td>0.408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>Consalve</td>\n",
       "      <td>Ballay</td>\n",
       "      <td>Male</td>\n",
       "      <td>41</td>\n",
       "      <td>1959-09-18</td>\n",
       "      <td>Web Developer I</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.7875</td>\n",
       "      <td>0.787500</td>\n",
       "      <td>0.669375</td>\n",
       "      <td>987.0</td>\n",
       "      <td>987</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>Lolly</td>\n",
       "      <td>Prewer</td>\n",
       "      <td>Female</td>\n",
       "      <td>44</td>\n",
       "      <td>1997-10-23</td>\n",
       "      <td>Geologist I</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.0700</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>0.909500</td>\n",
       "      <td>988.0</td>\n",
       "      <td>988</td>\n",
       "      <td>0.3995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>Vyky</td>\n",
       "      <td>Pegg</td>\n",
       "      <td>Female</td>\n",
       "      <td>64</td>\n",
       "      <td>1943-03-15</td>\n",
       "      <td>Quality Control Specialist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.962500</td>\n",
       "      <td>0.818125</td>\n",
       "      <td>988.0</td>\n",
       "      <td>988</td>\n",
       "      <td>0.3995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>Kellen</td>\n",
       "      <td>Pawelski</td>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>1945-07-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.2500</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>988.0</td>\n",
       "      <td>988</td>\n",
       "      <td>0.3995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>Jermaine</td>\n",
       "      <td>Bagshawe</td>\n",
       "      <td>Female</td>\n",
       "      <td>60</td>\n",
       "      <td>1954-05-14</td>\n",
       "      <td>Help Desk Operator</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.6375</td>\n",
       "      <td>0.637500</td>\n",
       "      <td>0.541875</td>\n",
       "      <td>988.0</td>\n",
       "      <td>988</td>\n",
       "      <td>0.3995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>Bryan</td>\n",
       "      <td>Jachtym</td>\n",
       "      <td>Male</td>\n",
       "      <td>59</td>\n",
       "      <td>1974-05-15</td>\n",
       "      <td>Automation Specialist I</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.8625</td>\n",
       "      <td>0.862500</td>\n",
       "      <td>0.733125</td>\n",
       "      <td>988.0</td>\n",
       "      <td>988</td>\n",
       "      <td>0.3995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>Renie</td>\n",
       "      <td>Laundon</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>1973-12-18</td>\n",
       "      <td>Assistant Media Planner</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.3500</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>1.147500</td>\n",
       "      <td>993.0</td>\n",
       "      <td>993</td>\n",
       "      <td>0.391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>Weidar</td>\n",
       "      <td>Etheridge</td>\n",
       "      <td>Male</td>\n",
       "      <td>38</td>\n",
       "      <td>1959-07-13</td>\n",
       "      <td>Compensation Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.1500</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>0.977500</td>\n",
       "      <td>994.0</td>\n",
       "      <td>994</td>\n",
       "      <td>0.3825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Datha</td>\n",
       "      <td>Fishburn</td>\n",
       "      <td>Female</td>\n",
       "      <td>15</td>\n",
       "      <td>1990-07-02</td>\n",
       "      <td>Office Assistant IV</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>12</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.7700</td>\n",
       "      <td>0.962500</td>\n",
       "      <td>0.818125</td>\n",
       "      <td>994.0</td>\n",
       "      <td>994</td>\n",
       "      <td>0.3825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Ferdinand</td>\n",
       "      <td>Romanetti</td>\n",
       "      <td>Male</td>\n",
       "      <td>60</td>\n",
       "      <td>1959-10-07</td>\n",
       "      <td>Paralegal</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.7900</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>996.0</td>\n",
       "      <td>996</td>\n",
       "      <td>0.374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Burk</td>\n",
       "      <td>Wortley</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>2001-10-17</td>\n",
       "      <td>Senior Sales Associate</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.7600</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.807500</td>\n",
       "      <td>997.0</td>\n",
       "      <td>997</td>\n",
       "      <td>0.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Melloney</td>\n",
       "      <td>Temby</td>\n",
       "      <td>Female</td>\n",
       "      <td>17</td>\n",
       "      <td>1954-10-05</td>\n",
       "      <td>Budget/Accounting Analyst IV</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.0625</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>997.0</td>\n",
       "      <td>997</td>\n",
       "      <td>0.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Dickie</td>\n",
       "      <td>Cubbini</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>1952-12-17</td>\n",
       "      <td>Financial Advisor</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.3625</td>\n",
       "      <td>1.362500</td>\n",
       "      <td>1.158125</td>\n",
       "      <td>997.0</td>\n",
       "      <td>997</td>\n",
       "      <td>0.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>Sylas</td>\n",
       "      <td>Duffill</td>\n",
       "      <td>Male</td>\n",
       "      <td>56</td>\n",
       "      <td>1955-10-02</td>\n",
       "      <td>Staff Accountant IV</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.5875</td>\n",
       "      <td>0.734375</td>\n",
       "      <td>0.624219</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          fname         lname  gender 3y_bike_purchases                  DOB  \\\n",
       "1       Chickie       Brister    Male                86           1957-07-12   \n",
       "2         Morly        Genery    Male                69           1970-03-22   \n",
       "3       Ardelis     Forrester  Female                10  1974-08-28 00:00:00   \n",
       "4        Lucine         Stutt  Female                64           1979-01-28   \n",
       "5       Melinda        Hadlee  Female                34           1965-09-21   \n",
       "6         Druci       Brandli  Female                39           1951-04-29   \n",
       "7      Rutledge         Hallt    Male                23           1976-10-06   \n",
       "8        Nancie          Vian  Female                74           1972-12-27   \n",
       "9          Duff     Karlowicz    Male                50           1972-04-28   \n",
       "10      Barthel        Docket    Male                72           1985-08-02   \n",
       "11     Rockwell        Matson    Male                94           1995-01-01   \n",
       "12      Wheeler       Winward    Male                48           1999-08-30   \n",
       "13         Olag           NaN    Male                60           1990-05-13   \n",
       "14        Melba      Spellacy  Female                38           1976-12-09   \n",
       "15       Mandie        Feares  Female                32           1964-04-19   \n",
       "16        Dukie         Swire    Male                88           1954-03-31   \n",
       "17     Marcelia     Monkleigh  Female                61           1993-08-22   \n",
       "18    Winnifred  Beswetherick  Female                83  1976-06-08 00:00:00   \n",
       "19       Odilia         Quick  Female                65           1938-11-09   \n",
       "20        Karly     Willavize  Female                 2           1954-08-12   \n",
       "21       Teddie      Burchill    Male                11           1968-12-21   \n",
       "22       Gaston     Dallaghan    Male                44           1993-09-29   \n",
       "23         Otis         Ottey    Male                26           1998-02-05   \n",
       "24     Tabbatha       Averill  Female                 5  1977-12-17 00:00:00   \n",
       "25        Brena     Schnitter  Female                78           1982-10-11   \n",
       "26       Rourke      Gillbard    Male                11           1945-08-03   \n",
       "27        Dyane       Burwell  Female                19           1952-06-27   \n",
       "28     Claudine      Barstowk  Female                71           1966-07-20   \n",
       "29      Blinnie          Roze  Female                84           1960-12-10   \n",
       "30        Rhona     De Freyne  Female                45           1960-11-22   \n",
       "...         ...           ...     ...               ...                  ...   \n",
       "971       Mavra         Finan  Female                30           1967-08-23   \n",
       "972      Frieda       Tavinor  Female                43           1999-03-04   \n",
       "973     Ellwood        Budden    Male                82           1998-06-03   \n",
       "974        Alex      Patshull  Female                37           1966-01-02   \n",
       "975     Aundrea      Outridge  Female                77           2001-01-24   \n",
       "976        Amby        Bodega    Male                63           1968-06-12   \n",
       "977        Esme      Pilipets    Male                15           1967-05-06   \n",
       "978     Beverly       Domnick  Female                56           1938-12-10   \n",
       "979     Artemis       Swanson    Male                77           1977-02-12   \n",
       "980      Daryle     Marginson    Male                93           1986-06-27   \n",
       "981        Tyne       Anshell  Female                71           1992-04-08   \n",
       "982       Leona      Shorrock  Female                83           1951-08-23   \n",
       "983   Bertrando        Carass    Male                45           1956-06-25   \n",
       "984     Augusta         Munns  Female                 5           1951-09-17   \n",
       "985     Pauline      Dallosso       U                82                  NaN   \n",
       "986    Lauralee         Fudge  Female                83           1987-09-21   \n",
       "987    Consalve        Ballay    Male                41           1959-09-18   \n",
       "988       Lolly        Prewer  Female                44           1997-10-23   \n",
       "989        Vyky          Pegg  Female                64           1943-03-15   \n",
       "990      Kellen      Pawelski  Female                83           1945-07-26   \n",
       "991    Jermaine      Bagshawe  Female                60           1954-05-14   \n",
       "992       Bryan       Jachtym    Male                59           1974-05-15   \n",
       "993       Renie       Laundon  Female                32           1973-12-18   \n",
       "994      Weidar     Etheridge    Male                38           1959-07-13   \n",
       "995       Datha      Fishburn  Female                15           1990-07-02   \n",
       "996   Ferdinand     Romanetti    Male                60           1959-10-07   \n",
       "997        Burk       Wortley    Male                22           2001-10-17   \n",
       "998    Melloney         Temby  Female                17           1954-10-05   \n",
       "999      Dickie       Cubbini    Male                30           1952-12-17   \n",
       "1000      Sylas       Duffill    Male                56           1955-10-02   \n",
       "\n",
       "                                JT            Category    wealth_segement  \\\n",
       "1                  General Manager       Manufacturing      Mass Customer   \n",
       "2              Structural Engineer            Property      Mass Customer   \n",
       "3           Senior Cost Accountant  Financial Services  Affluent Customer   \n",
       "4       Account Representative III       Manufacturing  Affluent Customer   \n",
       "5                Financial Analyst  Financial Services  Affluent Customer   \n",
       "6          Assistant Media Planner       Entertainment     High Net Worth   \n",
       "7             Compensation Analyst  Financial Services      Mass Customer   \n",
       "8     Human Resources Assistant II              Retail      Mass Customer   \n",
       "9               Speech Pathologist       Manufacturing      Mass Customer   \n",
       "10         Accounting Assistant IV                  IT      Mass Customer   \n",
       "11            Programmer Analyst I              Retail     High Net Worth   \n",
       "12        Environmental Specialist       Manufacturing      Mass Customer   \n",
       "13         Human Resources Manager  Telecommunications      Mass Customer   \n",
       "14                    VP Marketing              Health      Mass Customer   \n",
       "15             Clinical Specialist              Health      Mass Customer   \n",
       "16                             NaN       Manufacturing  Affluent Customer   \n",
       "17             Associate Professor       Manufacturing      Mass Customer   \n",
       "18                         Actuary  Financial Services      Mass Customer   \n",
       "19                 General Manager       Manufacturing  Affluent Customer   \n",
       "20                Internal Auditor       Manufacturing     High Net Worth   \n",
       "21                    Programmer I       Manufacturing      Mass Customer   \n",
       "22               Financial Analyst  Financial Services      Mass Customer   \n",
       "23                Quality Engineer                 NaN      Mass Customer   \n",
       "24      Quality Control Specialist                 NaN  Affluent Customer   \n",
       "25               Account Executive       Manufacturing      Mass Customer   \n",
       "26                             NaN            Property      Mass Customer   \n",
       "27                     Engineer II              Retail      Mass Customer   \n",
       "28                           Nurse            Property     High Net Worth   \n",
       "29                       Librarian       Entertainment     High Net Worth   \n",
       "30                             NaN              Health     High Net Worth   \n",
       "...                            ...                 ...                ...   \n",
       "971                          Nurse            Property  Affluent Customer   \n",
       "972                            NaN                 NaN  Affluent Customer   \n",
       "973                            NaN              Health      Mass Customer   \n",
       "974        Senior Quality Engineer              Retail      Mass Customer   \n",
       "975            Structural Engineer  Financial Services  Affluent Customer   \n",
       "976                      Recruiter                 NaN  Affluent Customer   \n",
       "977             Environmental Tech              Health      Mass Customer   \n",
       "978   Structural Analysis Engineer                  IT      Mass Customer   \n",
       "979                Web Designer II         Argiculture      Mass Customer   \n",
       "980             Environmental Tech         Argiculture      Mass Customer   \n",
       "981    Mechanical Systems Engineer                 NaN      Mass Customer   \n",
       "982        Senior Quality Engineer  Financial Services  Affluent Customer   \n",
       "983                Sales Associate            Property      Mass Customer   \n",
       "984     Quality Control Specialist                 NaN      Mass Customer   \n",
       "985     Desktop Support Technician                  IT  Affluent Customer   \n",
       "986                 Research Nurse              Health      Mass Customer   \n",
       "987                Web Developer I                  IT      Mass Customer   \n",
       "988                    Geologist I              Health      Mass Customer   \n",
       "989     Quality Control Specialist       Manufacturing      Mass Customer   \n",
       "990                            NaN       Manufacturing     High Net Worth   \n",
       "991             Help Desk Operator            Property      Mass Customer   \n",
       "992        Automation Specialist I       Manufacturing      Mass Customer   \n",
       "993        Assistant Media Planner       Entertainment      Mass Customer   \n",
       "994           Compensation Analyst  Financial Services      Mass Customer   \n",
       "995            Office Assistant IV              Retail      Mass Customer   \n",
       "996                      Paralegal  Financial Services  Affluent Customer   \n",
       "997         Senior Sales Associate              Health      Mass Customer   \n",
       "998   Budget/Accounting Analyst IV  Financial Services  Affluent Customer   \n",
       "999              Financial Advisor  Financial Services      Mass Customer   \n",
       "1000           Staff Accountant IV            Property      Mass Customer   \n",
       "\n",
       "     D_Indicator owns_car    ...    state    country prop_val Unnamed: 16  \\\n",
       "1              N      Yes    ...      QLD  Australia        6        0.56   \n",
       "2              N       No    ...      NSW  Australia       11        0.89   \n",
       "3              N       No    ...      VIC  Australia        5        1.01   \n",
       "4              N      Yes    ...      QLD  Australia        1        0.87   \n",
       "5              N       No    ...      NSW  Australia        9        0.52   \n",
       "6              N      Yes    ...      QLD  Australia        7        0.43   \n",
       "7              N       No    ...      NSW  Australia        7        0.40   \n",
       "8              N      Yes    ...      QLD  Australia        5        0.58   \n",
       "9              N      Yes    ...      NSW  Australia       10        1.03   \n",
       "10             N      Yes    ...      QLD  Australia        5        0.84   \n",
       "11             N       No    ...      QLD  Australia        6        0.58   \n",
       "12             N       No    ...      VIC  Australia        8        0.55   \n",
       "13             N       No    ...      NSW  Australia       11        0.43   \n",
       "14             N       No    ...      NSW  Australia       10        0.84   \n",
       "15             N       No    ...      QLD  Australia        8        0.80   \n",
       "16             N      Yes    ...      NSW  Australia        8        0.58   \n",
       "17             N      Yes    ...      QLD  Australia        6        0.86   \n",
       "18             N       No    ...      VIC  Australia       10        0.90   \n",
       "19             N      Yes    ...      NSW  Australia       11        0.53   \n",
       "20             N       No    ...      NSW  Australia       10        0.46   \n",
       "21             N      Yes    ...      NSW  Australia        9        0.85   \n",
       "22             N      Yes    ...      NSW  Australia        7        0.77   \n",
       "23             N       No    ...      QLD  Australia        4        1.02   \n",
       "24             N      Yes    ...      NSW  Australia        8        0.72   \n",
       "25             N       No    ...      QLD  Australia        5        0.61   \n",
       "26             N       No    ...      QLD  Australia        4        0.59   \n",
       "27             N      Yes    ...      NSW  Australia        2        0.89   \n",
       "28             N      Yes    ...      QLD  Australia       11        0.67   \n",
       "29             N       No    ...      QLD  Australia        7        1.08   \n",
       "30             N       No    ...      VIC  Australia       10        0.73   \n",
       "...          ...      ...    ...      ...        ...      ...         ...   \n",
       "971            N      Yes    ...      NSW  Australia        9        0.87   \n",
       "972            N       No    ...      VIC  Australia        6        0.49   \n",
       "973            N      Yes    ...      NSW  Australia       10        0.99   \n",
       "974            N      Yes    ...      QLD  Australia        5        0.71   \n",
       "975            N      Yes    ...      VIC  Australia       12        0.67   \n",
       "976            N      Yes    ...      VIC  Australia        6        0.49   \n",
       "977            N      Yes    ...      VIC  Australia        9        1.01   \n",
       "978            N       No    ...      NSW  Australia       10        0.48   \n",
       "979            N      Yes    ...      VIC  Australia        5        0.84   \n",
       "980            N      Yes    ...      NSW  Australia       12        0.44   \n",
       "981            N      Yes    ...      NSW  Australia        8        0.51   \n",
       "982            N      Yes    ...      QLD  Australia        1        0.44   \n",
       "983            N       No    ...      NSW  Australia        5        0.68   \n",
       "984            N       No    ...      NSW  Australia       11        0.87   \n",
       "985            N      Yes    ...      NSW  Australia       10        0.68   \n",
       "986            N       No    ...      VIC  Australia        4        0.60   \n",
       "987            N      Yes    ...      NSW  Australia        8        0.63   \n",
       "988            N       No    ...      NSW  Australia        7        1.07   \n",
       "989            N      Yes    ...      NSW  Australia        7        0.77   \n",
       "990            N      Yes    ...      NSW  Australia        8        1.00   \n",
       "991            N      Yes    ...      QLD  Australia        6        0.51   \n",
       "992            N      Yes    ...      VIC  Australia        3        0.69   \n",
       "993            N      Yes    ...      QLD  Australia        3        1.08   \n",
       "994            N      Yes    ...      NSW  Australia        4        0.92   \n",
       "995            N       No    ...      VIC  Australia       12        0.77   \n",
       "996            N       No    ...      NSW  Australia        7        0.79   \n",
       "997            N       No    ...      NSW  Australia       10        0.76   \n",
       "998            N      Yes    ...      QLD  Australia        2        0.85   \n",
       "999            N      Yes    ...      QLD  Australia        2        1.09   \n",
       "1000           N      Yes    ...      NSW  Australia        9        0.47   \n",
       "\n",
       "     Unnamed: 17 Unnamed: 18  Unnamed: 19  Unnamed: 20  rank     value  \n",
       "1         0.7000    0.875000     0.743750          1.0     1   1.71875  \n",
       "2         0.8900    1.112500     0.945625          1.0     1   1.71875  \n",
       "3         1.0100    1.010000     1.010000          1.0     1   1.71875  \n",
       "4         1.0875    1.087500     1.087500          4.0     4   1.70312  \n",
       "5         0.5200    0.650000     0.650000          4.0     4   1.70312  \n",
       "6         0.5375    0.537500     0.537500          6.0     6   1.67188  \n",
       "7         0.4000    0.400000     0.340000          6.0     6   1.67188  \n",
       "8         0.7250    0.725000     0.616250          8.0     8   1.65625  \n",
       "9         1.2875    1.609375     1.367969          8.0     8   1.65625  \n",
       "10        1.0500    1.050000     0.892500         10.0    10   1.64062  \n",
       "11        0.5800    0.725000     0.725000         10.0    10   1.64062  \n",
       "12        0.5500    0.550000     0.467500         12.0    12     1.625  \n",
       "13        0.4300    0.537500     0.456875         13.0    13   1.60938  \n",
       "14        0.8400    1.050000     0.892500         14.0    14   1.59375  \n",
       "15        0.8000    0.800000     0.680000         14.0    14   1.59375  \n",
       "16        0.7250    0.906250     0.906250         16.0    16    1.5625  \n",
       "17        1.0750    1.075000     0.913750         17.0    17   1.54688  \n",
       "18        0.9000    1.125000     0.956250         17.0    17   1.54688  \n",
       "19        0.6625    0.828125     0.828125         19.0    19   1.53125  \n",
       "20        0.4600    0.575000     0.575000         19.0    19   1.53125  \n",
       "21        1.0625    1.328125     1.128906         21.0    21   1.51562  \n",
       "22        0.9625    0.962500     0.818125         21.0    21   1.51562  \n",
       "23        1.0200    1.020000     0.867000         23.0    23       1.5  \n",
       "24        0.9000    0.900000     0.900000         23.0    23       1.5  \n",
       "25        0.6100    0.610000     0.518500         25.0    25   1.48438  \n",
       "26        0.5900    0.590000     0.501500         26.0    26   1.46875  \n",
       "27        1.1125    1.112500     0.945625         26.0    26   1.46875  \n",
       "28        0.8375    1.046875     1.046875         26.0    26   1.46875  \n",
       "29        1.0800    1.350000     1.350000         26.0    26   1.46875  \n",
       "30        0.7300    0.912500     0.912500         30.0    30   1.46094  \n",
       "...          ...         ...          ...          ...   ...       ...  \n",
       "971       1.0875    1.359375     1.359375        967.0   967  0.435625  \n",
       "972       0.4900    0.490000     0.490000        972.0   972      0.43  \n",
       "973       1.2375    1.546875     1.314844        972.0   972      0.43  \n",
       "974       0.8875    0.887500     0.754375        974.0   974     0.425  \n",
       "975       0.8375    1.046875     1.046875        974.0   974     0.425  \n",
       "976       0.6125    0.612500     0.612500        974.0   974     0.425  \n",
       "977       1.2625    1.578125     1.341406        977.0   977      0.42  \n",
       "978       0.4800    0.600000     0.510000        977.0   977      0.42  \n",
       "979       1.0500    1.050000     0.892500        979.0   979    0.4165  \n",
       "980       0.5500    0.687500     0.584375        979.0   979    0.4165  \n",
       "981       0.6375    0.637500     0.541875        979.0   979    0.4165  \n",
       "982       0.5500    0.687500     0.687500        979.0   979    0.4165  \n",
       "983       0.6800    0.680000     0.578000        983.0   983      0.41  \n",
       "984       0.8700    1.087500     0.924375        983.0   983      0.41  \n",
       "985       0.8500    1.062500     1.062500        985.0   985     0.408  \n",
       "986       0.6000    0.750000     0.637500        985.0   985     0.408  \n",
       "987       0.7875    0.787500     0.669375        987.0   987       0.4  \n",
       "988       1.0700    1.070000     0.909500        988.0   988    0.3995  \n",
       "989       0.9625    0.962500     0.818125        988.0   988    0.3995  \n",
       "990       1.2500    1.562500     1.562500        988.0   988    0.3995  \n",
       "991       0.6375    0.637500     0.541875        988.0   988    0.3995  \n",
       "992       0.8625    0.862500     0.733125        988.0   988    0.3995  \n",
       "993       1.3500    1.350000     1.147500        993.0   993     0.391  \n",
       "994       1.1500    1.150000     0.977500        994.0   994    0.3825  \n",
       "995       0.7700    0.962500     0.818125        994.0   994    0.3825  \n",
       "996       0.7900    0.790000     0.790000        996.0   996     0.374  \n",
       "997       0.7600    0.950000     0.807500        997.0   997     0.357  \n",
       "998       1.0625    1.062500     1.062500        997.0   997     0.357  \n",
       "999       1.3625    1.362500     1.158125        997.0   997     0.357  \n",
       "1000      0.5875    0.734375     0.624219       1000.0  1000      0.34  \n",
       "\n",
       "[1000 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename for easier analysis\n",
    "df.rename(columns={\"Note: The data and information in this document is reflective of a hypothetical situation and client. This document is to be used for KPMG Virtual Internship purposes only. \":\"fname\"}, inplace = True)\n",
    "df.rename(columns={\"Unnamed: 1\":\"lname\",\n",
    "                   \"Unnamed: 2\":\"gender\",\n",
    "                   \"Unnamed: 3\":\"3y_bike_purchases\",\n",
    "                   \"Unnamed: 4\":\"DOB\",\n",
    "                   \"Unnamed: 5\":\"JT\",\n",
    "                   \"Unnamed: 6\":\"Category\"}, inplace = True)\n",
    "df.rename(columns={\"Unnamed: 7\":\"wealth_segement\",\n",
    "                   \"Unnamed: 8\":\"D_Indicator\",\n",
    "                   \"Unnamed: 9\":\"owns_car\",\n",
    "                   \"Unnamed: 10\":\"tencure\",\n",
    "                   \"Unnamed: 11\":\"address\",\n",
    "                   \"Unnamed: 12\":\"postcode\"}, inplace = True)\n",
    "df.rename(columns={\"Unnamed: 13\":\"state\",\n",
    "                   \"Unnamed: 14\":\"country\",\n",
    "                   \"Unnamed: 15\":\"prop_val\",\n",
    "                   \"Unnamed: 21\":\"rank\",\n",
    "                   \"Unnamed: 22\":\"value\"}, inplace = True)\n",
    "df=df.iloc[1:]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 29, 0, 0, 17, 106, 165, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] 23\n"
     ]
    }
   ],
   "source": [
    "def check_NA():\n",
    "    ret = []\n",
    "    temp = list(df)\n",
    "    for each in temp:\n",
    "        ret.append(df[each].isna().sum())\n",
    "    return ret\n",
    "\n",
    "NaNlist = check_NA()\n",
    "print(NaNlist, len(NaNlist))\n",
    "# need to check columns\n",
    "check = []\n",
    "k     = -1\n",
    "for i in NaNlist:\n",
    "    k += 1\n",
    "    if i > 0:\n",
    "        check.append(k)\n",
    "def check_unique():\n",
    "    temp = list(df)\n",
    "    mydict = {}\n",
    "    for i in range(2, 23):\n",
    "        ct = 0\n",
    "        if NaNlist[i] > 0:\n",
    "            ct = len(df[temp[i]].unique()) - 1\n",
    "        else:\n",
    "            ct = len(df[temp[i]].unique())\n",
    "        mydict[temp[i]] = (ct, df[temp[i]].unique())\n",
    "    return mydict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, array(['Male', 'Female', 'U'], dtype=object))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_unique()[\"gender\"] #no F or M, easier to analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[513, 470, 17]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1f24e80a128>,\n",
       "  <matplotlib.axis.XTick at 0x1f2502ddfd0>,\n",
       "  <matplotlib.axis.XTick at 0x1f2502e20b8>,\n",
       "  <matplotlib.axis.XTick at 0x1f2500718d0>,\n",
       "  <matplotlib.axis.XTick at 0x1f250071f28>,\n",
       "  <matplotlib.axis.XTick at 0x1f2500575f8>],\n",
       " <a list of 6 Text xticklabel objects>)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f25054c0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analyze the Gender vs Bikes bought\n",
    "#array(['F', 'Male', 'Female', 'U', 'Femal', 'M'] from KPMG_VI.py\n",
    "gender = [0,0,0] #first is Female, second if Male, third is Unknown\n",
    "for each in df['gender']:\n",
    "    if each[0] == \"F\":\n",
    "        gender[0] += 1\n",
    "    elif each[0] == \"M\":\n",
    "        gender[1] += 1\n",
    "    else:\n",
    "        gender[2] += 1\n",
    "\n",
    "print(gender)\n",
    "def gf(x, pos):\n",
    "    #'The two args are the value and gender'\n",
    "    return int(x)\n",
    "\n",
    "bike = [0,0,0]\n",
    "df[['3y_bike_purchases']] = df[['3y_bike_purchases']].apply(pd.to_numeric)\n",
    "bike[0] += df['3y_bike_purchases'][df['gender'] == 'Female'].sum() \n",
    "bike[1] += df['3y_bike_purchases'][df['gender'] == 'Male'].sum()\n",
    "bike[2] += df['3y_bike_purchases'][df['gender'] == 'U'].sum()\n",
    "\n",
    "grapho = [gender[0], bike[0], gender[1], bike[1], gender[2], bike[2]]\n",
    "graphl = [gender[0]*10, bike[0], gender[1]*10, bike[1], gender[2]*10, bike[2]]\n",
    "colorr = (0.3,0.1,0.4,0.6)\n",
    "#scale up gender by 10 for easier visualization\n",
    "formatter = FuncFormatter(gf)\n",
    "x = np.arange(6)\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_ylabel('Number of people')\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "plt.bar(x, graphl, color = colorr)\n",
    "for i in range(len(gender*2)):\n",
    "    plt.text(x = i-0.3, y = graphl[i]+0.1, s = grapho[i], size = 10)\n",
    "\n",
    "plt.xticks(x, ('Female', \"F's P\", 'Male', \"M's P\", 'Unknown', \"U's P\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1f250557d68>,\n",
       "  <matplotlib.axis.XTick at 0x1f250447b00>,\n",
       "  <matplotlib.axis.XTick at 0x1f250583d68>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f24e7fa748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg  = [0,0,0]\n",
    "ss   = sum(bike)\n",
    "for i in range(len(avg)):\n",
    "    avg[i] += round(bike[i]/ss,3) \n",
    "\n",
    "x1 = np.arange(3)\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.set_ylabel('Percentage bikes bought by gender')\n",
    "plt.bar(x1, avg, color = colorr)\n",
    "for i in range(len(avg)):\n",
    "    plt.text(x = i-0.15, y = avg[i], s = avg[i], size = 10)\n",
    "\n",
    "plt.xticks(x1, ('Female', 'Male', 'Unknown'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(961, array(['1957-07-12', '1970-03-22', datetime.datetime(1974, 8, 28, 0, 0),\n",
       "        '1979-01-28', '1965-09-21', '1951-04-29', '1976-10-06',\n",
       "        '1972-12-27', '1972-04-28', '1985-08-02', '1995-01-01',\n",
       "        '1999-08-30', '1990-05-13', '1976-12-09', '1964-04-19',\n",
       "        '1954-03-31', '1993-08-22', datetime.datetime(1976, 6, 8, 0, 0),\n",
       "        '1938-11-09', '1954-08-12', '1968-12-21', '1993-09-29',\n",
       "        '1998-02-05', datetime.datetime(1977, 12, 17, 0, 0), '1982-10-11',\n",
       "        '1945-08-03', '1952-06-27', '1966-07-20', '1960-12-10',\n",
       "        '1960-11-22', '1980-01-26', '1951-11-28', '1987-01-15',\n",
       "        '1981-09-22', datetime.datetime(1977, 5, 9, 0, 0), '1975-05-10',\n",
       "        '1968-03-25', '1979-04-11', '1955-06-13', '1993-08-28',\n",
       "        '1953-02-13', '1985-03-14', '1946-10-25', '1966-09-18',\n",
       "        '1961-12-05', '1956-08-30', '1956-07-16', '1994-12-04',\n",
       "        '1977-05-14', '1950-05-03', datetime.datetime(1978, 2, 11, 0, 0),\n",
       "        '1999-03-22', '1993-12-19', '1981-12-01', '1995-03-25',\n",
       "        '1981-02-03', '1979-12-17', '1972-10-31', '1966-07-29', nan,\n",
       "        '1983-02-11', '1971-12-29', '1942-02-06', '1967-03-18',\n",
       "        '1999-11-21', '1983-02-01', '1996-07-20', '1984-10-07',\n",
       "        '1999-06-08', '1988-09-18', '1996-08-03', '1994-08-04',\n",
       "        '1999-09-21', '1968-02-16', '1958-04-21', '1974-09-17',\n",
       "        '1961-04-15', '1956-10-22', '1972-01-14', '1973-11-28',\n",
       "        '1995-04-09', '1965-09-08', '1981-02-22',\n",
       "        datetime.datetime(1975, 9, 12, 0, 0), '1961-03-22', '1949-04-29',\n",
       "        '1999-02-15', '1942-11-01', '1950-02-10', '1971-08-18',\n",
       "        '1992-04-19', datetime.datetime(1978, 2, 19, 0, 0), '1977-09-01',\n",
       "        '1952-09-21', datetime.datetime(1978, 12, 14, 0, 0), '1999-10-20',\n",
       "        '1943-02-08', '1994-10-31', '1972-11-02', '2000-05-09',\n",
       "        '1991-01-22', '1994-02-11', '1940-01-18', '1980-10-20',\n",
       "        '1961-10-20', '1946-10-23', '1963-06-16', '1972-04-23',\n",
       "        '1961-10-22', '1969-11-09', '1968-11-15', '1939-12-09',\n",
       "        '1961-11-05', '1995-08-13', datetime.datetime(1977, 11, 8, 0, 0),\n",
       "        '1997-11-13', '1946-07-09', '1978-01-11', '1987-10-08',\n",
       "        '1952-12-14', '1981-10-29', '1997-05-28', '1969-07-20',\n",
       "        '1975-03-13', '1941-02-22', '1956-02-08', '1953-02-27',\n",
       "        '1975-03-10', '1940-02-02', '1990-07-12', '1962-07-28',\n",
       "        '1965-07-03', '1960-03-18', '1985-07-23', '1978-02-05',\n",
       "        '1970-11-08', '1990-05-29', '1967-09-27', '1979-06-18',\n",
       "        '1980-02-11', '1966-07-16', '2001-03-08', '1949-03-06',\n",
       "        '1975-12-27', '1981-08-08', '1950-11-05', '1965-07-15',\n",
       "        '1969-10-02', '1969-05-21', '1971-03-31', '1967-12-18',\n",
       "        '1961-12-26', '1969-02-10', '1961-08-14', '1956-09-04',\n",
       "        '1963-04-24', datetime.datetime(1976, 10, 24, 0, 0), '1957-05-25',\n",
       "        '1978-01-15', '1951-06-11', '1986-08-07', '1995-08-04',\n",
       "        '1969-08-27', '1951-07-10', '1979-05-22', '1976-03-12',\n",
       "        '1973-03-18', '1989-01-05', '1952-07-01', '1986-01-23',\n",
       "        '1955-06-05', '1956-02-22', '1989-11-18', '1995-02-19',\n",
       "        '1955-09-06', '1992-07-05', '1939-01-29', '1952-04-01',\n",
       "        '1950-12-26', '1975-03-26', '1965-01-08', '1974-12-09',\n",
       "        '1946-03-24', '1974-03-25', '1985-09-02', '1992-07-27',\n",
       "        '1971-11-28', datetime.datetime(1973, 5, 9, 0, 0), '1986-11-09',\n",
       "        '1996-05-30', '1992-03-27', '1938-10-16', '1970-03-19',\n",
       "        '1969-08-02', '1944-01-05', '1972-03-10', '1981-02-16',\n",
       "        '1990-01-09', '1993-11-18', '1966-01-02', '1940-12-05',\n",
       "        '1959-04-19', '1945-02-13', '1952-10-20', '1998-11-26',\n",
       "        '1948-10-12', '1976-08-27', '1955-08-18', '1958-01-03',\n",
       "        '1967-05-25', '1967-12-12', '1983-11-26', '1997-03-13',\n",
       "        '1956-10-16', '1947-02-28', '2000-04-07', '1997-09-04',\n",
       "        '1963-01-01', '1980-02-16', '1994-04-15', '1981-06-20',\n",
       "        datetime.datetime(1979, 2, 5, 0, 0), '1966-01-08', '2000-01-25',\n",
       "        datetime.datetime(1977, 12, 6, 0, 0), '2000-06-25', '1964-06-22',\n",
       "        '1954-11-13', '1963-12-13', '1978-05-31', '1943-09-24',\n",
       "        '1965-08-15', '1943-07-14', '1997-07-23', '1940-07-13',\n",
       "        '1943-10-05', '1939-04-04', '1941-10-02', '1961-05-27',\n",
       "        '1974-01-24', '1959-05-08', '1969-09-21', '1957-11-15',\n",
       "        '1950-09-05', '1967-09-11', '1963-05-02', '1971-04-26',\n",
       "        '1978-12-11', '1974-05-14', '1975-03-12', '1967-02-03',\n",
       "        '1961-01-16', '1980-09-16', '1997-09-25', '1939-07-17',\n",
       "        '1959-04-11', '1962-03-03', '2000-09-25', '1971-11-27',\n",
       "        '1969-07-06', '1991-06-07', '1992-11-18', '1948-01-13',\n",
       "        '1996-02-19', '1938-08-31', '1969-11-17', '1973-09-25',\n",
       "        '1951-12-16', '1972-01-17', '1996-07-01', '1961-01-03',\n",
       "        '1976-08-24', '1956-06-28', datetime.datetime(1976, 6, 11, 0, 0),\n",
       "        '1966-03-07', '1953-03-12', '1962-10-06',\n",
       "        datetime.datetime(1976, 3, 7, 0, 0), '1968-10-14', '1971-09-18',\n",
       "        datetime.datetime(1978, 4, 2, 0, 0), '1959-01-05', '1967-01-09',\n",
       "        '1957-03-23', '1947-04-22', '1958-12-28', '1976-04-20',\n",
       "        '1990-11-13', '1981-06-03', '1972-07-30', '1958-12-07',\n",
       "        '1963-05-11', '1980-02-01', '1988-01-30', '1970-08-08',\n",
       "        '1965-02-27', '1991-02-14', '1947-10-08', '1988-01-10',\n",
       "        '1993-08-09', '1981-08-05', '1949-05-20', '1955-07-17',\n",
       "        '1974-08-09', '1954-11-09', '1959-06-12', '1981-02-15',\n",
       "        '1971-08-19', '1991-02-07', '1965-12-13', '1989-12-15',\n",
       "        '1968-11-24', '1992-01-31', '1978-05-13', '1957-10-17',\n",
       "        '1985-03-26', '1955-07-28', '1981-09-11',\n",
       "        datetime.datetime(1976, 11, 6, 0, 0), '1977-04-08', '1981-11-29',\n",
       "        '1992-08-27', '1955-01-08', '1952-05-19', '1982-02-10',\n",
       "        datetime.datetime(1978, 7, 22, 0, 0), '1972-11-26', '1999-06-23',\n",
       "        '1959-12-25', '1997-09-27', '1978-05-27', '1945-08-08',\n",
       "        '1943-08-27', '1999-10-24', '1976-01-24', '1956-06-19',\n",
       "        '1975-03-28', '1969-10-09', '1968-12-11', '1951-11-27',\n",
       "        '1939-12-22', '1964-09-28', '1952-04-15', '1941-07-21',\n",
       "        '1961-02-11', '1953-08-08', '1981-05-26', '1956-12-02',\n",
       "        '1964-10-01', '1944-10-21', '1953-10-19', '2002-02-27',\n",
       "        '1980-07-30', '1944-11-10', '1971-01-11', '1990-07-28',\n",
       "        '1979-10-31', '1997-12-26', '1957-09-03', '1999-11-08',\n",
       "        '1981-08-17', '1971-12-24', '1959-06-13', '1957-10-01',\n",
       "        '1994-10-21', datetime.datetime(1978, 8, 2, 0, 0), '1963-08-20',\n",
       "        '1974-07-05', '1975-08-04', '1960-09-12',\n",
       "        datetime.datetime(1974, 11, 9, 0, 0), '1986-12-03', '1974-06-24',\n",
       "        '1942-01-23', '1963-01-14', '1996-01-24', '1964-01-31',\n",
       "        '1944-10-05', '1995-05-18', '1954-06-14', '1961-09-29',\n",
       "        '1973-08-25', '1993-03-02', '1948-06-30',\n",
       "        datetime.datetime(1979, 2, 26, 0, 0), '1939-08-28', '1982-12-10',\n",
       "        '1950-04-26', '1990-05-20', '1961-12-22', '2002-01-17',\n",
       "        '1961-01-05', '1942-08-09', '1979-03-24', '1976-11-04',\n",
       "        '1967-01-20', '1955-02-13', datetime.datetime(1978, 1, 21, 0, 0),\n",
       "        '1977-09-10', '1980-08-15', '1938-11-29', '1996-02-29',\n",
       "        '1983-03-08', '1957-12-05', '1946-03-15', '1995-12-09',\n",
       "        '1954-07-18', '1958-08-28', '1938-09-10', '1963-10-15',\n",
       "        '1953-05-17', '1989-01-14', '1998-01-12', '1991-12-18',\n",
       "        '1948-01-24', '1970-01-21', '1991-12-01', '1993-10-18',\n",
       "        '1945-11-08', '1947-07-19', '1975-01-08', '1969-02-09',\n",
       "        datetime.datetime(1977, 9, 26, 0, 0), '1940-11-10', '1974-12-12',\n",
       "        '1975-12-22', '1957-11-12', '1978-10-26',\n",
       "        datetime.datetime(1973, 11, 14, 0, 0), '1997-08-27', '1974-08-07',\n",
       "        '1952-10-05', '1941-04-04', '1997-03-17', '1987-03-01',\n",
       "        '1964-01-22', '1972-05-17', '1993-11-27', '1958-04-19',\n",
       "        '1966-04-30', '1977-02-03', '1998-11-22', '1996-11-24',\n",
       "        '1991-12-02', '1943-04-06', '1982-02-01', '1985-01-29',\n",
       "        '1961-07-31', '1976-09-07', '1972-08-27', '1947-03-11',\n",
       "        '1974-11-25', '1940-05-19', '1947-11-26', '1965-02-03',\n",
       "        '1977-03-20', '1993-08-03', '1972-05-19', '1970-12-17',\n",
       "        '2000-12-09', '2001-08-04', '1990-10-19', '1967-03-14',\n",
       "        '1999-11-29', '1956-04-21', '1979-07-28', '2001-12-29',\n",
       "        '1972-07-29', '1995-10-03', '1957-02-14', '1951-04-30',\n",
       "        '1959-03-01', '1939-03-06', '1961-08-27', '1970-01-14',\n",
       "        '1944-10-25', '1966-06-27', '1966-09-14', '1994-03-13',\n",
       "        '1975-06-15', '1975-10-12', '1965-05-28', '1996-02-11',\n",
       "        '1944-06-14', '1977-11-08', '1997-11-01', '1952-05-05',\n",
       "        '1960-05-09', '1989-03-17', '1954-10-21', '1967-02-02',\n",
       "        '1976-09-20', datetime.datetime(1974, 8, 19, 0, 0), '1960-12-19',\n",
       "        datetime.datetime(1975, 9, 11, 0, 0), '1945-07-10', '1980-09-01',\n",
       "        datetime.datetime(1974, 8, 29, 0, 0),\n",
       "        datetime.datetime(1976, 11, 24, 0, 0), '1980-03-14', '1973-10-13',\n",
       "        '1999-05-14', datetime.datetime(1976, 3, 10, 0, 0), '1954-01-30',\n",
       "        '1988-09-30', '1971-06-28', '2001-06-21', '1964-05-15',\n",
       "        '1999-07-28', '1954-10-06', '1951-10-14', '1956-05-17',\n",
       "        '1991-01-23', '1950-09-30', '1980-05-29', '1954-03-10',\n",
       "        '1964-11-06', '1953-05-24', '1972-06-27',\n",
       "        datetime.datetime(1973, 7, 3, 0, 0), '1998-12-19', '1981-09-14',\n",
       "        '1986-01-10', '1955-04-08', '1952-12-04', '1956-05-29',\n",
       "        datetime.datetime(1978, 12, 10, 0, 0), '1994-11-16', '1978-12-14',\n",
       "        '1997-11-29', '1965-04-22', '1976-09-25',\n",
       "        datetime.datetime(1975, 4, 8, 0, 0), '1950-11-30', '1998-02-19',\n",
       "        '1977-06-30', '1972-04-27', '1973-01-06', '1984-07-06',\n",
       "        '1991-04-21', datetime.datetime(1979, 1, 3, 0, 0), '1976-01-16',\n",
       "        '1999-04-21', '1958-05-14', '1995-10-01', '1993-11-02',\n",
       "        '1972-03-24', '1994-02-04', '1947-04-21', '1950-03-31',\n",
       "        '1951-01-29', '1954-03-08', '1994-06-16', '1967-04-07',\n",
       "        '1955-07-06', '1955-11-25', '1964-03-14', '1986-09-25',\n",
       "        '1991-11-24', '2001-09-27', '1968-07-29', '1950-12-09',\n",
       "        '1942-08-23', '1965-02-25', '1998-05-12', '1943-10-27',\n",
       "        '1976-08-12', '1994-10-15', '1973-01-04', '1990-07-06',\n",
       "        '1995-07-18', '1995-12-17', '1951-10-28', '1938-08-30',\n",
       "        '1996-04-13', '1994-04-17', '1974-12-25', '1987-04-20',\n",
       "        '1979-10-22', '1996-05-10', '1965-02-09', '1955-10-22',\n",
       "        datetime.datetime(1973, 3, 15, 0, 0), '1958-07-19',\n",
       "        datetime.datetime(1978, 12, 31, 0, 0), '1994-11-07', '1939-05-26',\n",
       "        '1996-01-21', '1997-06-29', '1973-01-29', '1961-05-23',\n",
       "        '1966-09-19', '1955-07-13', '1979-09-30', '1970-05-19',\n",
       "        '1998-08-24', datetime.datetime(1976, 12, 7, 0, 0), '1969-03-05',\n",
       "        '1950-05-08', '1951-03-19', '1957-06-10', '1963-07-31',\n",
       "        '1956-07-05', datetime.datetime(1973, 10, 3, 0, 0), '1959-01-08',\n",
       "        '1968-12-26', '1955-09-29', '1940-06-07', '1998-01-15',\n",
       "        '1967-01-19', '1960-04-12', '1993-09-20', '1973-07-13',\n",
       "        '1961-04-20', '1939-08-22', '1945-12-24', '1987-05-01',\n",
       "        '1962-09-21', '1975-10-31', '1997-05-18', '1954-01-06',\n",
       "        '1950-03-18', '1978-04-04', '1954-10-19', '1950-05-04',\n",
       "        '1966-08-04', '1968-02-17', '1982-12-15', '1996-05-28',\n",
       "        '1962-07-12', datetime.datetime(1976, 10, 8, 0, 0), '1940-05-23',\n",
       "        '1995-07-19', '1981-07-06', '1942-10-21', '1958-06-25',\n",
       "        '1988-01-07', '1976-10-29', '1997-04-03', '1958-04-25',\n",
       "        '1944-04-28', '1985-03-25', '1942-07-06',\n",
       "        datetime.datetime(1977, 9, 11, 0, 0), '1968-07-12', '1991-12-21',\n",
       "        '1959-07-28', '1980-10-29', '1940-10-26', '1960-01-21',\n",
       "        '1960-02-14', '1941-02-26', '1976-07-16',\n",
       "        datetime.datetime(1974, 10, 12, 0, 0), '1959-08-01', '1956-12-04',\n",
       "        '1944-03-18', '1956-03-19', '1940-03-11', '1962-08-05',\n",
       "        '1952-04-07', '1966-08-11', '1996-09-06', '1970-09-09',\n",
       "        '1996-06-18', '1947-12-28', '1981-05-05', '1978-03-27',\n",
       "        '1977-07-19', '1980-04-09', '1994-12-23', '1941-02-23',\n",
       "        '1992-10-10', '1938-09-02', '1992-06-24', '1978-04-20',\n",
       "        '1973-08-30', '1954-06-15', datetime.datetime(1977, 10, 7, 0, 0),\n",
       "        '1991-06-01', '1950-11-06', '1957-12-10', '1961-11-03',\n",
       "        '1974-07-03', '1981-01-29', datetime.datetime(1974, 12, 25, 0, 0),\n",
       "        '1965-09-04', '1973-04-29', '1953-08-07', '1951-07-22',\n",
       "        '1975-11-18', '1946-08-22', '1999-11-18', '1972-09-04',\n",
       "        '1983-12-10', '1983-05-18', '1971-10-11', '1975-07-09',\n",
       "        '1950-10-12', '1963-10-16', '1965-09-09', '1966-10-18',\n",
       "        '1999-11-30', '1966-01-30', '1972-10-23', '1965-08-21',\n",
       "        datetime.datetime(1977, 1, 3, 0, 0), '1969-03-06', '1970-03-11',\n",
       "        '1997-10-23', '1956-09-15', '1961-12-10', '1997-03-28',\n",
       "        '1992-04-07', '1945-06-11', '1991-03-06', '1981-04-14',\n",
       "        '1990-04-04', '1966-09-17', '1963-09-01', '1975-09-20',\n",
       "        '1947-06-12', '1939-09-09', '1974-02-21', '2001-04-16',\n",
       "        '1948-03-02', '1998-08-19', '1976-07-19', '1980-01-03',\n",
       "        '1962-05-12', datetime.datetime(1975, 7, 25, 0, 0), '1971-01-10',\n",
       "        '1950-04-15', '1960-07-04', '1965-03-15', '1974-06-17',\n",
       "        '1969-06-04', '1996-03-18', '1973-09-29', '1982-04-25',\n",
       "        '1978-06-21', datetime.datetime(1973, 12, 22, 0, 0), '1987-11-02',\n",
       "        '1950-09-09', '2002-01-04', '1983-02-08', '1960-10-01',\n",
       "        '1999-02-22', '1990-04-06', '1965-12-22', '1938-08-05',\n",
       "        '1947-12-15', '1984-12-13', '1953-04-02', '1989-07-23',\n",
       "        '1993-06-02', '1988-03-05', '1966-01-19', '1972-11-10',\n",
       "        '1996-04-05', '1981-07-28', '1964-07-07', '1955-01-15',\n",
       "        '1992-10-13', '1994-04-12', '1956-08-12', '1957-09-04',\n",
       "        '1982-03-20', '1980-04-23', '1974-01-26', '1973-03-12',\n",
       "        '1954-09-21', '1955-11-09', '1951-08-25', '1969-10-03',\n",
       "        '1966-11-17', '1938-06-08', '1981-07-05', '1949-08-11',\n",
       "        '1982-03-09', '1974-05-21', '1980-09-14', '1951-02-05',\n",
       "        '1947-07-13', '1952-05-30', '1964-11-10', '1940-11-25',\n",
       "        '1985-12-19', '1961-01-15', '1968-11-23', '1978-07-10',\n",
       "        '1991-02-06', '1952-12-09', '2000-04-10', '1960-04-23',\n",
       "        '1940-05-18', '1965-05-04', '1988-12-15', '1988-09-01',\n",
       "        '1978-08-06', '1966-11-05', '1977-01-28', '1973-05-11',\n",
       "        '1971-05-31', '1995-10-10', '2000-04-17', '1963-12-08',\n",
       "        '1992-03-09', '1962-05-05', '1973-08-11', '1967-10-05',\n",
       "        '1962-08-26', '1963-08-25', datetime.datetime(1979, 1, 9, 0, 0),\n",
       "        '1951-09-16', '1994-07-17', '1939-02-19', '1948-01-01',\n",
       "        '1979-09-29', '1958-04-16', '2001-04-17', '1973-04-14',\n",
       "        '1994-09-30', '1995-10-19', '1982-10-29', '1997-03-07',\n",
       "        '1957-01-24', '1959-11-13', '1956-11-07', '1998-08-16',\n",
       "        '1942-09-10', '1980-09-13', datetime.datetime(1978, 12, 27, 0, 0),\n",
       "        '1953-03-27', '1939-11-08', '1958-03-29', '1979-02-10',\n",
       "        '1980-11-26', '1953-08-21', '2000-05-20', '1990-09-15',\n",
       "        '1947-04-06', '1964-12-07', '1958-07-28', '1974-07-28',\n",
       "        '1967-06-01', '1961-08-23', '1953-10-12',\n",
       "        datetime.datetime(1974, 6, 8, 0, 0),\n",
       "        datetime.datetime(1974, 9, 28, 0, 0), '1995-10-20', '1956-03-08',\n",
       "        '1948-08-03', '1994-04-11', '1966-04-07', '1973-10-10',\n",
       "        '1997-03-03', '1964-11-02', '1938-06-09', '1994-03-29',\n",
       "        '1957-03-17', '1980-07-28', '1971-10-18', '1956-10-05',\n",
       "        '1972-07-02', '1999-06-18', '1984-09-01', '2000-03-24',\n",
       "        '1961-10-02', '1941-06-17', '1959-09-18', '1952-08-21',\n",
       "        '1968-05-28', '1970-12-30', '1964-05-19', '1996-02-15',\n",
       "        '1973-05-13', datetime.datetime(1973, 3, 24, 0, 0), '1967-03-02',\n",
       "        '1963-03-04', '2000-05-06', '1957-11-17', '1975-09-16',\n",
       "        '1991-05-21', '1960-12-27', '1961-02-10', '1939-01-15',\n",
       "        '1987-11-16', '1979-05-16', '1984-05-15', '1988-10-16',\n",
       "        '1950-06-15', '1958-05-15', '1990-12-21', '1981-04-06',\n",
       "        '1983-11-06', '1973-05-30', '1959-08-20', '1957-07-03',\n",
       "        datetime.datetime(1978, 3, 17, 0, 0), '1993-08-11', '1951-01-18',\n",
       "        '1976-07-25', '1987-09-09', datetime.datetime(1975, 1, 19, 0, 0),\n",
       "        '1960-08-31', '1963-08-10', '1964-08-23', '1945-10-31',\n",
       "        '1940-03-18', '1946-09-09', datetime.datetime(1974, 4, 30, 0, 0),\n",
       "        '1954-09-06', datetime.datetime(1974, 5, 28, 0, 0), '1992-08-09',\n",
       "        '1941-08-19', '1988-08-22', '1992-03-26', '1994-02-10',\n",
       "        '1983-06-26', '1976-11-25', '1966-03-03', '1977-12-08',\n",
       "        '1962-09-17', '1994-11-28', '1969-04-29', '1964-02-01',\n",
       "        '1998-10-14', '1967-01-03', '1975-07-30', '1969-01-05',\n",
       "        '1980-12-19', '1975-02-25', '1994-06-25', '1960-08-10',\n",
       "        '1986-01-17', '1967-08-23', '1999-03-04', '1998-06-03',\n",
       "        '2001-01-24', '1968-06-12', '1967-05-06', '1938-12-10',\n",
       "        '1977-02-12', '1986-06-27', '1992-04-08', '1951-08-23',\n",
       "        '1956-06-25', '1951-09-17', '1987-09-21', '1943-03-15',\n",
       "        '1945-07-26', '1954-05-14', '1974-05-15', '1973-12-18',\n",
       "        '1959-07-13', '1990-07-02', '1959-10-07', '2001-10-17',\n",
       "        '1954-10-05', '1952-12-17', '1955-10-02'], dtype=object))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_unique()[\"DOB\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1974\n",
      "1976\n",
      "1977\n",
      "1977\n",
      "1978\n",
      "1975\n",
      "1978\n",
      "1978\n",
      "1977\n",
      "1976\n",
      "1973\n",
      "1979\n",
      "1977\n",
      "1976\n",
      "1976\n",
      "1978\n",
      "1976\n",
      "1978\n",
      "1978\n",
      "1974\n",
      "1979\n",
      "1978\n",
      "1977\n",
      "1973\n",
      "1974\n",
      "1975\n",
      "1974\n",
      "1976\n",
      "1976\n",
      "1973\n",
      "1978\n",
      "1975\n",
      "1979\n",
      "1973\n",
      "1978\n",
      "1976\n",
      "1973\n",
      "1976\n",
      "1977\n",
      "1974\n",
      "1977\n",
      "1974\n",
      "1977\n",
      "1975\n",
      "1973\n",
      "1979\n",
      "1978\n",
      "1974\n",
      "1974\n",
      "1973\n",
      "1978\n",
      "1975\n",
      "1974\n",
      "1974\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fname</th>\n",
       "      <th>lname</th>\n",
       "      <th>gender</th>\n",
       "      <th>3y_bike_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>JT</th>\n",
       "      <th>Category</th>\n",
       "      <th>wealth_segement</th>\n",
       "      <th>D_Indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>...</th>\n",
       "      <th>country</th>\n",
       "      <th>prop_val</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "      <th>Unnamed: 19</th>\n",
       "      <th>Unnamed: 20</th>\n",
       "      <th>rank</th>\n",
       "      <th>value</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chickie</td>\n",
       "      <td>Brister</td>\n",
       "      <td>Male</td>\n",
       "      <td>86</td>\n",
       "      <td>1957-07-12</td>\n",
       "      <td>General Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.7000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.743750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.71875</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Morly</td>\n",
       "      <td>Genery</td>\n",
       "      <td>Male</td>\n",
       "      <td>69</td>\n",
       "      <td>1970-03-22</td>\n",
       "      <td>Structural Engineer</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.8900</td>\n",
       "      <td>1.112500</td>\n",
       "      <td>0.945625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.71875</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ardelis</td>\n",
       "      <td>Forrester</td>\n",
       "      <td>Female</td>\n",
       "      <td>10</td>\n",
       "      <td>1974-08-28 00:00:00</td>\n",
       "      <td>Senior Cost Accountant</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.0100</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.71875</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lucine</td>\n",
       "      <td>Stutt</td>\n",
       "      <td>Female</td>\n",
       "      <td>64</td>\n",
       "      <td>1979-01-28</td>\n",
       "      <td>Account Representative III</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.0875</td>\n",
       "      <td>1.087500</td>\n",
       "      <td>1.087500</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.70312</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Melinda</td>\n",
       "      <td>Hadlee</td>\n",
       "      <td>Female</td>\n",
       "      <td>34</td>\n",
       "      <td>1965-09-21</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.5200</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.70312</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Druci</td>\n",
       "      <td>Brandli</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1951-04-29</td>\n",
       "      <td>Assistant Media Planner</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.5375</td>\n",
       "      <td>0.537500</td>\n",
       "      <td>0.537500</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.67188</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Rutledge</td>\n",
       "      <td>Hallt</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>1976-10-06</td>\n",
       "      <td>Compensation Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.67188</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nancie</td>\n",
       "      <td>Vian</td>\n",
       "      <td>Female</td>\n",
       "      <td>74</td>\n",
       "      <td>1972-12-27</td>\n",
       "      <td>Human Resources Assistant II</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.7250</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.616250</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.65625</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Duff</td>\n",
       "      <td>Karlowicz</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>1972-04-28</td>\n",
       "      <td>Speech Pathologist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.2875</td>\n",
       "      <td>1.609375</td>\n",
       "      <td>1.367969</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.65625</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Barthel</td>\n",
       "      <td>Docket</td>\n",
       "      <td>Male</td>\n",
       "      <td>72</td>\n",
       "      <td>1985-08-02</td>\n",
       "      <td>Accounting Assistant IV</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.0500</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.892500</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.64062</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rockwell</td>\n",
       "      <td>Matson</td>\n",
       "      <td>Male</td>\n",
       "      <td>94</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>Programmer Analyst I</td>\n",
       "      <td>Retail</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.5800</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.64062</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Wheeler</td>\n",
       "      <td>Winward</td>\n",
       "      <td>Male</td>\n",
       "      <td>48</td>\n",
       "      <td>1999-08-30</td>\n",
       "      <td>Environmental Specialist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.467500</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1.625</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Olag</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>60</td>\n",
       "      <td>1990-05-13</td>\n",
       "      <td>Human Resources Manager</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.4300</td>\n",
       "      <td>0.537500</td>\n",
       "      <td>0.456875</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1.60938</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Melba</td>\n",
       "      <td>Spellacy</td>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "      <td>1976-12-09</td>\n",
       "      <td>VP Marketing</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.8400</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.892500</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1.59375</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mandie</td>\n",
       "      <td>Feares</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>1964-04-19</td>\n",
       "      <td>Clinical Specialist</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.8000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1.59375</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dukie</td>\n",
       "      <td>Swire</td>\n",
       "      <td>Male</td>\n",
       "      <td>88</td>\n",
       "      <td>1954-03-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.7250</td>\n",
       "      <td>0.906250</td>\n",
       "      <td>0.906250</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1.5625</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Marcelia</td>\n",
       "      <td>Monkleigh</td>\n",
       "      <td>Female</td>\n",
       "      <td>61</td>\n",
       "      <td>1993-08-22</td>\n",
       "      <td>Associate Professor</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.0750</td>\n",
       "      <td>1.075000</td>\n",
       "      <td>0.913750</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.54688</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Winnifred</td>\n",
       "      <td>Beswetherick</td>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>1976-06-08 00:00:00</td>\n",
       "      <td>Actuary</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>1.125000</td>\n",
       "      <td>0.956250</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17</td>\n",
       "      <td>1.54688</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Odilia</td>\n",
       "      <td>Quick</td>\n",
       "      <td>Female</td>\n",
       "      <td>65</td>\n",
       "      <td>1938-11-09</td>\n",
       "      <td>General Manager</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.6625</td>\n",
       "      <td>0.828125</td>\n",
       "      <td>0.828125</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19</td>\n",
       "      <td>1.53125</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Karly</td>\n",
       "      <td>Willavize</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>1954-08-12</td>\n",
       "      <td>Internal Auditor</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.4600</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19</td>\n",
       "      <td>1.53125</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Teddie</td>\n",
       "      <td>Burchill</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>1968-12-21</td>\n",
       "      <td>Programmer I</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.0625</td>\n",
       "      <td>1.328125</td>\n",
       "      <td>1.128906</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1.51562</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Gaston</td>\n",
       "      <td>Dallaghan</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1993-09-29</td>\n",
       "      <td>Financial Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.962500</td>\n",
       "      <td>0.818125</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21</td>\n",
       "      <td>1.51562</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Otis</td>\n",
       "      <td>Ottey</td>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>1998-02-05</td>\n",
       "      <td>Quality Engineer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.0200</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>0.867000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tabbatha</td>\n",
       "      <td>Averill</td>\n",
       "      <td>Female</td>\n",
       "      <td>5</td>\n",
       "      <td>1977-12-17 00:00:00</td>\n",
       "      <td>Quality Control Specialist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1.5</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Brena</td>\n",
       "      <td>Schnitter</td>\n",
       "      <td>Female</td>\n",
       "      <td>78</td>\n",
       "      <td>1982-10-11</td>\n",
       "      <td>Account Executive</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.6100</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>0.518500</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.48438</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Rourke</td>\n",
       "      <td>Gillbard</td>\n",
       "      <td>Male</td>\n",
       "      <td>11</td>\n",
       "      <td>1945-08-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.5900</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.501500</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1.46875</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Dyane</td>\n",
       "      <td>Burwell</td>\n",
       "      <td>Female</td>\n",
       "      <td>19</td>\n",
       "      <td>1952-06-27</td>\n",
       "      <td>Engineer II</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.1125</td>\n",
       "      <td>1.112500</td>\n",
       "      <td>0.945625</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1.46875</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Claudine</td>\n",
       "      <td>Barstowk</td>\n",
       "      <td>Female</td>\n",
       "      <td>71</td>\n",
       "      <td>1966-07-20</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>Property</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.8375</td>\n",
       "      <td>1.046875</td>\n",
       "      <td>1.046875</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1.46875</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Blinnie</td>\n",
       "      <td>Roze</td>\n",
       "      <td>Female</td>\n",
       "      <td>84</td>\n",
       "      <td>1960-12-10</td>\n",
       "      <td>Librarian</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.0800</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26</td>\n",
       "      <td>1.46875</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Rhona</td>\n",
       "      <td>De Freyne</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>1960-11-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Health</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.7300</td>\n",
       "      <td>0.912500</td>\n",
       "      <td>0.912500</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30</td>\n",
       "      <td>1.46094</td>\n",
       "      <td>59</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>Mavra</td>\n",
       "      <td>Finan</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>1967-08-23</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>Property</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.0875</td>\n",
       "      <td>1.359375</td>\n",
       "      <td>1.359375</td>\n",
       "      <td>967.0</td>\n",
       "      <td>967</td>\n",
       "      <td>0.435625</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>Frieda</td>\n",
       "      <td>Tavinor</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>1999-03-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.4900</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>972.0</td>\n",
       "      <td>972</td>\n",
       "      <td>0.43</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>Ellwood</td>\n",
       "      <td>Budden</td>\n",
       "      <td>Male</td>\n",
       "      <td>82</td>\n",
       "      <td>1998-06-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.2375</td>\n",
       "      <td>1.546875</td>\n",
       "      <td>1.314844</td>\n",
       "      <td>972.0</td>\n",
       "      <td>972</td>\n",
       "      <td>0.43</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>Alex</td>\n",
       "      <td>Patshull</td>\n",
       "      <td>Female</td>\n",
       "      <td>37</td>\n",
       "      <td>1966-01-02</td>\n",
       "      <td>Senior Quality Engineer</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.887500</td>\n",
       "      <td>0.754375</td>\n",
       "      <td>974.0</td>\n",
       "      <td>974</td>\n",
       "      <td>0.425</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>Aundrea</td>\n",
       "      <td>Outridge</td>\n",
       "      <td>Female</td>\n",
       "      <td>77</td>\n",
       "      <td>2001-01-24</td>\n",
       "      <td>Structural Engineer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>12</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.8375</td>\n",
       "      <td>1.046875</td>\n",
       "      <td>1.046875</td>\n",
       "      <td>974.0</td>\n",
       "      <td>974</td>\n",
       "      <td>0.425</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>Amby</td>\n",
       "      <td>Bodega</td>\n",
       "      <td>Male</td>\n",
       "      <td>63</td>\n",
       "      <td>1968-06-12</td>\n",
       "      <td>Recruiter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.6125</td>\n",
       "      <td>0.612500</td>\n",
       "      <td>0.612500</td>\n",
       "      <td>974.0</td>\n",
       "      <td>974</td>\n",
       "      <td>0.425</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>Esme</td>\n",
       "      <td>Pilipets</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>1967-05-06</td>\n",
       "      <td>Environmental Tech</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.2625</td>\n",
       "      <td>1.578125</td>\n",
       "      <td>1.341406</td>\n",
       "      <td>977.0</td>\n",
       "      <td>977</td>\n",
       "      <td>0.42</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>Beverly</td>\n",
       "      <td>Domnick</td>\n",
       "      <td>Female</td>\n",
       "      <td>56</td>\n",
       "      <td>1938-12-10</td>\n",
       "      <td>Structural Analysis Engineer</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.4800</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>977.0</td>\n",
       "      <td>977</td>\n",
       "      <td>0.42</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>Artemis</td>\n",
       "      <td>Swanson</td>\n",
       "      <td>Male</td>\n",
       "      <td>77</td>\n",
       "      <td>1977-02-12</td>\n",
       "      <td>Web Designer II</td>\n",
       "      <td>Argiculture</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.0500</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.892500</td>\n",
       "      <td>979.0</td>\n",
       "      <td>979</td>\n",
       "      <td>0.4165</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>Daryle</td>\n",
       "      <td>Marginson</td>\n",
       "      <td>Male</td>\n",
       "      <td>93</td>\n",
       "      <td>1986-06-27</td>\n",
       "      <td>Environmental Tech</td>\n",
       "      <td>Argiculture</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>12</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.584375</td>\n",
       "      <td>979.0</td>\n",
       "      <td>979</td>\n",
       "      <td>0.4165</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>Tyne</td>\n",
       "      <td>Anshell</td>\n",
       "      <td>Female</td>\n",
       "      <td>71</td>\n",
       "      <td>1992-04-08</td>\n",
       "      <td>Mechanical Systems Engineer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.6375</td>\n",
       "      <td>0.637500</td>\n",
       "      <td>0.541875</td>\n",
       "      <td>979.0</td>\n",
       "      <td>979</td>\n",
       "      <td>0.4165</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>Leona</td>\n",
       "      <td>Shorrock</td>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>1951-08-23</td>\n",
       "      <td>Senior Quality Engineer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>979.0</td>\n",
       "      <td>979</td>\n",
       "      <td>0.4165</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>Bertrando</td>\n",
       "      <td>Carass</td>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>1956-06-25</td>\n",
       "      <td>Sales Associate</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.6800</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.578000</td>\n",
       "      <td>983.0</td>\n",
       "      <td>983</td>\n",
       "      <td>0.41</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>Augusta</td>\n",
       "      <td>Munns</td>\n",
       "      <td>Female</td>\n",
       "      <td>5</td>\n",
       "      <td>1951-09-17</td>\n",
       "      <td>Quality Control Specialist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>1.087500</td>\n",
       "      <td>0.924375</td>\n",
       "      <td>983.0</td>\n",
       "      <td>983</td>\n",
       "      <td>0.41</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>Pauline</td>\n",
       "      <td>Dallosso</td>\n",
       "      <td>U</td>\n",
       "      <td>82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Desktop Support Technician</td>\n",
       "      <td>IT</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>985.0</td>\n",
       "      <td>985</td>\n",
       "      <td>0.408</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>Lauralee</td>\n",
       "      <td>Fudge</td>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>1987-09-21</td>\n",
       "      <td>Research Nurse</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.637500</td>\n",
       "      <td>985.0</td>\n",
       "      <td>985</td>\n",
       "      <td>0.408</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>Consalve</td>\n",
       "      <td>Ballay</td>\n",
       "      <td>Male</td>\n",
       "      <td>41</td>\n",
       "      <td>1959-09-18</td>\n",
       "      <td>Web Developer I</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.7875</td>\n",
       "      <td>0.787500</td>\n",
       "      <td>0.669375</td>\n",
       "      <td>987.0</td>\n",
       "      <td>987</td>\n",
       "      <td>0.4</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>Lolly</td>\n",
       "      <td>Prewer</td>\n",
       "      <td>Female</td>\n",
       "      <td>44</td>\n",
       "      <td>1997-10-23</td>\n",
       "      <td>Geologist I</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.0700</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>0.909500</td>\n",
       "      <td>988.0</td>\n",
       "      <td>988</td>\n",
       "      <td>0.3995</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>Vyky</td>\n",
       "      <td>Pegg</td>\n",
       "      <td>Female</td>\n",
       "      <td>64</td>\n",
       "      <td>1943-03-15</td>\n",
       "      <td>Quality Control Specialist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.962500</td>\n",
       "      <td>0.818125</td>\n",
       "      <td>988.0</td>\n",
       "      <td>988</td>\n",
       "      <td>0.3995</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>Kellen</td>\n",
       "      <td>Pawelski</td>\n",
       "      <td>Female</td>\n",
       "      <td>83</td>\n",
       "      <td>1945-07-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>8</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.2500</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>988.0</td>\n",
       "      <td>988</td>\n",
       "      <td>0.3995</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>Jermaine</td>\n",
       "      <td>Bagshawe</td>\n",
       "      <td>Female</td>\n",
       "      <td>60</td>\n",
       "      <td>1954-05-14</td>\n",
       "      <td>Help Desk Operator</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>6</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.6375</td>\n",
       "      <td>0.637500</td>\n",
       "      <td>0.541875</td>\n",
       "      <td>988.0</td>\n",
       "      <td>988</td>\n",
       "      <td>0.3995</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>Bryan</td>\n",
       "      <td>Jachtym</td>\n",
       "      <td>Male</td>\n",
       "      <td>59</td>\n",
       "      <td>1974-05-15</td>\n",
       "      <td>Automation Specialist I</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.8625</td>\n",
       "      <td>0.862500</td>\n",
       "      <td>0.733125</td>\n",
       "      <td>988.0</td>\n",
       "      <td>988</td>\n",
       "      <td>0.3995</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>Renie</td>\n",
       "      <td>Laundon</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>1973-12-18</td>\n",
       "      <td>Assistant Media Planner</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.3500</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>1.147500</td>\n",
       "      <td>993.0</td>\n",
       "      <td>993</td>\n",
       "      <td>0.391</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>Weidar</td>\n",
       "      <td>Etheridge</td>\n",
       "      <td>Male</td>\n",
       "      <td>38</td>\n",
       "      <td>1959-07-13</td>\n",
       "      <td>Compensation Analyst</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.1500</td>\n",
       "      <td>1.150000</td>\n",
       "      <td>0.977500</td>\n",
       "      <td>994.0</td>\n",
       "      <td>994</td>\n",
       "      <td>0.3825</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Datha</td>\n",
       "      <td>Fishburn</td>\n",
       "      <td>Female</td>\n",
       "      <td>15</td>\n",
       "      <td>1990-07-02</td>\n",
       "      <td>Office Assistant IV</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>12</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.7700</td>\n",
       "      <td>0.962500</td>\n",
       "      <td>0.818125</td>\n",
       "      <td>994.0</td>\n",
       "      <td>994</td>\n",
       "      <td>0.3825</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Ferdinand</td>\n",
       "      <td>Romanetti</td>\n",
       "      <td>Male</td>\n",
       "      <td>60</td>\n",
       "      <td>1959-10-07</td>\n",
       "      <td>Paralegal</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.7900</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>996.0</td>\n",
       "      <td>996</td>\n",
       "      <td>0.374</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Burk</td>\n",
       "      <td>Wortley</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>2001-10-17</td>\n",
       "      <td>Senior Sales Associate</td>\n",
       "      <td>Health</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.7600</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.807500</td>\n",
       "      <td>997.0</td>\n",
       "      <td>997</td>\n",
       "      <td>0.357</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Melloney</td>\n",
       "      <td>Temby</td>\n",
       "      <td>Female</td>\n",
       "      <td>17</td>\n",
       "      <td>1954-10-05</td>\n",
       "      <td>Budget/Accounting Analyst IV</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.0625</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>1.062500</td>\n",
       "      <td>997.0</td>\n",
       "      <td>997</td>\n",
       "      <td>0.357</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Dickie</td>\n",
       "      <td>Cubbini</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>1952-12-17</td>\n",
       "      <td>Financial Advisor</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.3625</td>\n",
       "      <td>1.362500</td>\n",
       "      <td>1.158125</td>\n",
       "      <td>997.0</td>\n",
       "      <td>997</td>\n",
       "      <td>0.357</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>Sylas</td>\n",
       "      <td>Duffill</td>\n",
       "      <td>Male</td>\n",
       "      <td>56</td>\n",
       "      <td>1955-10-02</td>\n",
       "      <td>Staff Accountant IV</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.5875</td>\n",
       "      <td>0.734375</td>\n",
       "      <td>0.624219</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          fname         lname  gender  3y_bike_purchases                  DOB  \\\n",
       "1       Chickie       Brister    Male                 86           1957-07-12   \n",
       "2         Morly        Genery    Male                 69           1970-03-22   \n",
       "3       Ardelis     Forrester  Female                 10  1974-08-28 00:00:00   \n",
       "4        Lucine         Stutt  Female                 64           1979-01-28   \n",
       "5       Melinda        Hadlee  Female                 34           1965-09-21   \n",
       "6         Druci       Brandli  Female                 39           1951-04-29   \n",
       "7      Rutledge         Hallt    Male                 23           1976-10-06   \n",
       "8        Nancie          Vian  Female                 74           1972-12-27   \n",
       "9          Duff     Karlowicz    Male                 50           1972-04-28   \n",
       "10      Barthel        Docket    Male                 72           1985-08-02   \n",
       "11     Rockwell        Matson    Male                 94           1995-01-01   \n",
       "12      Wheeler       Winward    Male                 48           1999-08-30   \n",
       "13         Olag           NaN    Male                 60           1990-05-13   \n",
       "14        Melba      Spellacy  Female                 38           1976-12-09   \n",
       "15       Mandie        Feares  Female                 32           1964-04-19   \n",
       "16        Dukie         Swire    Male                 88           1954-03-31   \n",
       "17     Marcelia     Monkleigh  Female                 61           1993-08-22   \n",
       "18    Winnifred  Beswetherick  Female                 83  1976-06-08 00:00:00   \n",
       "19       Odilia         Quick  Female                 65           1938-11-09   \n",
       "20        Karly     Willavize  Female                  2           1954-08-12   \n",
       "21       Teddie      Burchill    Male                 11           1968-12-21   \n",
       "22       Gaston     Dallaghan    Male                 44           1993-09-29   \n",
       "23         Otis         Ottey    Male                 26           1998-02-05   \n",
       "24     Tabbatha       Averill  Female                  5  1977-12-17 00:00:00   \n",
       "25        Brena     Schnitter  Female                 78           1982-10-11   \n",
       "26       Rourke      Gillbard    Male                 11           1945-08-03   \n",
       "27        Dyane       Burwell  Female                 19           1952-06-27   \n",
       "28     Claudine      Barstowk  Female                 71           1966-07-20   \n",
       "29      Blinnie          Roze  Female                 84           1960-12-10   \n",
       "30        Rhona     De Freyne  Female                 45           1960-11-22   \n",
       "...         ...           ...     ...                ...                  ...   \n",
       "971       Mavra         Finan  Female                 30           1967-08-23   \n",
       "972      Frieda       Tavinor  Female                 43           1999-03-04   \n",
       "973     Ellwood        Budden    Male                 82           1998-06-03   \n",
       "974        Alex      Patshull  Female                 37           1966-01-02   \n",
       "975     Aundrea      Outridge  Female                 77           2001-01-24   \n",
       "976        Amby        Bodega    Male                 63           1968-06-12   \n",
       "977        Esme      Pilipets    Male                 15           1967-05-06   \n",
       "978     Beverly       Domnick  Female                 56           1938-12-10   \n",
       "979     Artemis       Swanson    Male                 77           1977-02-12   \n",
       "980      Daryle     Marginson    Male                 93           1986-06-27   \n",
       "981        Tyne       Anshell  Female                 71           1992-04-08   \n",
       "982       Leona      Shorrock  Female                 83           1951-08-23   \n",
       "983   Bertrando        Carass    Male                 45           1956-06-25   \n",
       "984     Augusta         Munns  Female                  5           1951-09-17   \n",
       "985     Pauline      Dallosso       U                 82                  NaN   \n",
       "986    Lauralee         Fudge  Female                 83           1987-09-21   \n",
       "987    Consalve        Ballay    Male                 41           1959-09-18   \n",
       "988       Lolly        Prewer  Female                 44           1997-10-23   \n",
       "989        Vyky          Pegg  Female                 64           1943-03-15   \n",
       "990      Kellen      Pawelski  Female                 83           1945-07-26   \n",
       "991    Jermaine      Bagshawe  Female                 60           1954-05-14   \n",
       "992       Bryan       Jachtym    Male                 59           1974-05-15   \n",
       "993       Renie       Laundon  Female                 32           1973-12-18   \n",
       "994      Weidar     Etheridge    Male                 38           1959-07-13   \n",
       "995       Datha      Fishburn  Female                 15           1990-07-02   \n",
       "996   Ferdinand     Romanetti    Male                 60           1959-10-07   \n",
       "997        Burk       Wortley    Male                 22           2001-10-17   \n",
       "998    Melloney         Temby  Female                 17           1954-10-05   \n",
       "999      Dickie       Cubbini    Male                 30           1952-12-17   \n",
       "1000      Sylas       Duffill    Male                 56           1955-10-02   \n",
       "\n",
       "                                JT            Category    wealth_segement  \\\n",
       "1                  General Manager       Manufacturing      Mass Customer   \n",
       "2              Structural Engineer            Property      Mass Customer   \n",
       "3           Senior Cost Accountant  Financial Services  Affluent Customer   \n",
       "4       Account Representative III       Manufacturing  Affluent Customer   \n",
       "5                Financial Analyst  Financial Services  Affluent Customer   \n",
       "6          Assistant Media Planner       Entertainment     High Net Worth   \n",
       "7             Compensation Analyst  Financial Services      Mass Customer   \n",
       "8     Human Resources Assistant II              Retail      Mass Customer   \n",
       "9               Speech Pathologist       Manufacturing      Mass Customer   \n",
       "10         Accounting Assistant IV                  IT      Mass Customer   \n",
       "11            Programmer Analyst I              Retail     High Net Worth   \n",
       "12        Environmental Specialist       Manufacturing      Mass Customer   \n",
       "13         Human Resources Manager  Telecommunications      Mass Customer   \n",
       "14                    VP Marketing              Health      Mass Customer   \n",
       "15             Clinical Specialist              Health      Mass Customer   \n",
       "16                             NaN       Manufacturing  Affluent Customer   \n",
       "17             Associate Professor       Manufacturing      Mass Customer   \n",
       "18                         Actuary  Financial Services      Mass Customer   \n",
       "19                 General Manager       Manufacturing  Affluent Customer   \n",
       "20                Internal Auditor       Manufacturing     High Net Worth   \n",
       "21                    Programmer I       Manufacturing      Mass Customer   \n",
       "22               Financial Analyst  Financial Services      Mass Customer   \n",
       "23                Quality Engineer                 NaN      Mass Customer   \n",
       "24      Quality Control Specialist                 NaN  Affluent Customer   \n",
       "25               Account Executive       Manufacturing      Mass Customer   \n",
       "26                             NaN            Property      Mass Customer   \n",
       "27                     Engineer II              Retail      Mass Customer   \n",
       "28                           Nurse            Property     High Net Worth   \n",
       "29                       Librarian       Entertainment     High Net Worth   \n",
       "30                             NaN              Health     High Net Worth   \n",
       "...                            ...                 ...                ...   \n",
       "971                          Nurse            Property  Affluent Customer   \n",
       "972                            NaN                 NaN  Affluent Customer   \n",
       "973                            NaN              Health      Mass Customer   \n",
       "974        Senior Quality Engineer              Retail      Mass Customer   \n",
       "975            Structural Engineer  Financial Services  Affluent Customer   \n",
       "976                      Recruiter                 NaN  Affluent Customer   \n",
       "977             Environmental Tech              Health      Mass Customer   \n",
       "978   Structural Analysis Engineer                  IT      Mass Customer   \n",
       "979                Web Designer II         Argiculture      Mass Customer   \n",
       "980             Environmental Tech         Argiculture      Mass Customer   \n",
       "981    Mechanical Systems Engineer                 NaN      Mass Customer   \n",
       "982        Senior Quality Engineer  Financial Services  Affluent Customer   \n",
       "983                Sales Associate            Property      Mass Customer   \n",
       "984     Quality Control Specialist                 NaN      Mass Customer   \n",
       "985     Desktop Support Technician                  IT  Affluent Customer   \n",
       "986                 Research Nurse              Health      Mass Customer   \n",
       "987                Web Developer I                  IT      Mass Customer   \n",
       "988                    Geologist I              Health      Mass Customer   \n",
       "989     Quality Control Specialist       Manufacturing      Mass Customer   \n",
       "990                            NaN       Manufacturing     High Net Worth   \n",
       "991             Help Desk Operator            Property      Mass Customer   \n",
       "992        Automation Specialist I       Manufacturing      Mass Customer   \n",
       "993        Assistant Media Planner       Entertainment      Mass Customer   \n",
       "994           Compensation Analyst  Financial Services      Mass Customer   \n",
       "995            Office Assistant IV              Retail      Mass Customer   \n",
       "996                      Paralegal  Financial Services  Affluent Customer   \n",
       "997         Senior Sales Associate              Health      Mass Customer   \n",
       "998   Budget/Accounting Analyst IV  Financial Services  Affluent Customer   \n",
       "999              Financial Advisor  Financial Services      Mass Customer   \n",
       "1000           Staff Accountant IV            Property      Mass Customer   \n",
       "\n",
       "     D_Indicator owns_car ...     country prop_val Unnamed: 16 Unnamed: 17  \\\n",
       "1              N      Yes ...   Australia        6        0.56      0.7000   \n",
       "2              N       No ...   Australia       11        0.89      0.8900   \n",
       "3              N       No ...   Australia        5        1.01      1.0100   \n",
       "4              N      Yes ...   Australia        1        0.87      1.0875   \n",
       "5              N       No ...   Australia        9        0.52      0.5200   \n",
       "6              N      Yes ...   Australia        7        0.43      0.5375   \n",
       "7              N       No ...   Australia        7        0.40      0.4000   \n",
       "8              N      Yes ...   Australia        5        0.58      0.7250   \n",
       "9              N      Yes ...   Australia       10        1.03      1.2875   \n",
       "10             N      Yes ...   Australia        5        0.84      1.0500   \n",
       "11             N       No ...   Australia        6        0.58      0.5800   \n",
       "12             N       No ...   Australia        8        0.55      0.5500   \n",
       "13             N       No ...   Australia       11        0.43      0.4300   \n",
       "14             N       No ...   Australia       10        0.84      0.8400   \n",
       "15             N       No ...   Australia        8        0.80      0.8000   \n",
       "16             N      Yes ...   Australia        8        0.58      0.7250   \n",
       "17             N      Yes ...   Australia        6        0.86      1.0750   \n",
       "18             N       No ...   Australia       10        0.90      0.9000   \n",
       "19             N      Yes ...   Australia       11        0.53      0.6625   \n",
       "20             N       No ...   Australia       10        0.46      0.4600   \n",
       "21             N      Yes ...   Australia        9        0.85      1.0625   \n",
       "22             N      Yes ...   Australia        7        0.77      0.9625   \n",
       "23             N       No ...   Australia        4        1.02      1.0200   \n",
       "24             N      Yes ...   Australia        8        0.72      0.9000   \n",
       "25             N       No ...   Australia        5        0.61      0.6100   \n",
       "26             N       No ...   Australia        4        0.59      0.5900   \n",
       "27             N      Yes ...   Australia        2        0.89      1.1125   \n",
       "28             N      Yes ...   Australia       11        0.67      0.8375   \n",
       "29             N       No ...   Australia        7        1.08      1.0800   \n",
       "30             N       No ...   Australia       10        0.73      0.7300   \n",
       "...          ...      ... ...         ...      ...         ...         ...   \n",
       "971            N      Yes ...   Australia        9        0.87      1.0875   \n",
       "972            N       No ...   Australia        6        0.49      0.4900   \n",
       "973            N      Yes ...   Australia       10        0.99      1.2375   \n",
       "974            N      Yes ...   Australia        5        0.71      0.8875   \n",
       "975            N      Yes ...   Australia       12        0.67      0.8375   \n",
       "976            N      Yes ...   Australia        6        0.49      0.6125   \n",
       "977            N      Yes ...   Australia        9        1.01      1.2625   \n",
       "978            N       No ...   Australia       10        0.48      0.4800   \n",
       "979            N      Yes ...   Australia        5        0.84      1.0500   \n",
       "980            N      Yes ...   Australia       12        0.44      0.5500   \n",
       "981            N      Yes ...   Australia        8        0.51      0.6375   \n",
       "982            N      Yes ...   Australia        1        0.44      0.5500   \n",
       "983            N       No ...   Australia        5        0.68      0.6800   \n",
       "984            N       No ...   Australia       11        0.87      0.8700   \n",
       "985            N      Yes ...   Australia       10        0.68      0.8500   \n",
       "986            N       No ...   Australia        4        0.60      0.6000   \n",
       "987            N      Yes ...   Australia        8        0.63      0.7875   \n",
       "988            N       No ...   Australia        7        1.07      1.0700   \n",
       "989            N      Yes ...   Australia        7        0.77      0.9625   \n",
       "990            N      Yes ...   Australia        8        1.00      1.2500   \n",
       "991            N      Yes ...   Australia        6        0.51      0.6375   \n",
       "992            N      Yes ...   Australia        3        0.69      0.8625   \n",
       "993            N      Yes ...   Australia        3        1.08      1.3500   \n",
       "994            N      Yes ...   Australia        4        0.92      1.1500   \n",
       "995            N       No ...   Australia       12        0.77      0.7700   \n",
       "996            N       No ...   Australia        7        0.79      0.7900   \n",
       "997            N       No ...   Australia       10        0.76      0.7600   \n",
       "998            N      Yes ...   Australia        2        0.85      1.0625   \n",
       "999            N      Yes ...   Australia        2        1.09      1.3625   \n",
       "1000           N      Yes ...   Australia        9        0.47      0.5875   \n",
       "\n",
       "     Unnamed: 18 Unnamed: 19  Unnamed: 20  rank     value  Age  \n",
       "1       0.875000    0.743750          1.0     1   1.71875   62  \n",
       "2       1.112500    0.945625          1.0     1   1.71875   49  \n",
       "3       1.010000    1.010000          1.0     1   1.71875   45  \n",
       "4       1.087500    1.087500          4.0     4   1.70312   40  \n",
       "5       0.650000    0.650000          4.0     4   1.70312   54  \n",
       "6       0.537500    0.537500          6.0     6   1.67188   68  \n",
       "7       0.400000    0.340000          6.0     6   1.67188   43  \n",
       "8       0.725000    0.616250          8.0     8   1.65625   47  \n",
       "9       1.609375    1.367969          8.0     8   1.65625   47  \n",
       "10      1.050000    0.892500         10.0    10   1.64062   34  \n",
       "11      0.725000    0.725000         10.0    10   1.64062   24  \n",
       "12      0.550000    0.467500         12.0    12     1.625   20  \n",
       "13      0.537500    0.456875         13.0    13   1.60938   29  \n",
       "14      1.050000    0.892500         14.0    14   1.59375   43  \n",
       "15      0.800000    0.680000         14.0    14   1.59375   55  \n",
       "16      0.906250    0.906250         16.0    16    1.5625   65  \n",
       "17      1.075000    0.913750         17.0    17   1.54688   26  \n",
       "18      1.125000    0.956250         17.0    17   1.54688   43  \n",
       "19      0.828125    0.828125         19.0    19   1.53125   81  \n",
       "20      0.575000    0.575000         19.0    19   1.53125   65  \n",
       "21      1.328125    1.128906         21.0    21   1.51562   51  \n",
       "22      0.962500    0.818125         21.0    21   1.51562   26  \n",
       "23      1.020000    0.867000         23.0    23       1.5   21  \n",
       "24      0.900000    0.900000         23.0    23       1.5   42  \n",
       "25      0.610000    0.518500         25.0    25   1.48438   37  \n",
       "26      0.590000    0.501500         26.0    26   1.46875   74  \n",
       "27      1.112500    0.945625         26.0    26   1.46875   67  \n",
       "28      1.046875    1.046875         26.0    26   1.46875   53  \n",
       "29      1.350000    1.350000         26.0    26   1.46875   59  \n",
       "30      0.912500    0.912500         30.0    30   1.46094   59  \n",
       "...          ...         ...          ...   ...       ...  ...  \n",
       "971     1.359375    1.359375        967.0   967  0.435625   52  \n",
       "972     0.490000    0.490000        972.0   972      0.43   20  \n",
       "973     1.546875    1.314844        972.0   972      0.43   21  \n",
       "974     0.887500    0.754375        974.0   974     0.425   53  \n",
       "975     1.046875    1.046875        974.0   974     0.425   18  \n",
       "976     0.612500    0.612500        974.0   974     0.425   51  \n",
       "977     1.578125    1.341406        977.0   977      0.42   52  \n",
       "978     0.600000    0.510000        977.0   977      0.42   81  \n",
       "979     1.050000    0.892500        979.0   979    0.4165   42  \n",
       "980     0.687500    0.584375        979.0   979    0.4165   33  \n",
       "981     0.637500    0.541875        979.0   979    0.4165   27  \n",
       "982     0.687500    0.687500        979.0   979    0.4165   68  \n",
       "983     0.680000    0.578000        983.0   983      0.41   63  \n",
       "984     1.087500    0.924375        983.0   983      0.41   68  \n",
       "985     1.062500    1.062500        985.0   985     0.408    0  \n",
       "986     0.750000    0.637500        985.0   985     0.408   32  \n",
       "987     0.787500    0.669375        987.0   987       0.4   60  \n",
       "988     1.070000    0.909500        988.0   988    0.3995   22  \n",
       "989     0.962500    0.818125        988.0   988    0.3995   76  \n",
       "990     1.562500    1.562500        988.0   988    0.3995   74  \n",
       "991     0.637500    0.541875        988.0   988    0.3995   65  \n",
       "992     0.862500    0.733125        988.0   988    0.3995   45  \n",
       "993     1.350000    1.147500        993.0   993     0.391   46  \n",
       "994     1.150000    0.977500        994.0   994    0.3825   60  \n",
       "995     0.962500    0.818125        994.0   994    0.3825   29  \n",
       "996     0.790000    0.790000        996.0   996     0.374   60  \n",
       "997     0.950000    0.807500        997.0   997     0.357   18  \n",
       "998     1.062500    1.062500        997.0   997     0.357   65  \n",
       "999     1.362500    1.158125        997.0   997     0.357   67  \n",
       "1000    0.734375    0.624219       1000.0  1000      0.34    0  \n",
       "\n",
       "[1000 rows x 24 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analyze on the age vs bikes bought\n",
    "#Needs to transform\n",
    "df[\"Age\"] = 0\n",
    "lenn = len(df[\"DOB\"])\n",
    "k    = 0\n",
    "for i in range(1, lenn):\n",
    "    #type is date\n",
    "    if isinstance(df[\"DOB\"][i], datetime.date):\n",
    "        tl = len(df[\"DOB\"][i].ctime().split(\" \"))\n",
    "        print(int(df[\"DOB\"][i].ctime().split(\" \")[tl-1]))\n",
    "        df[\"Age\"][i] += int(2019 - int(df[\"DOB\"][i].ctime().split(\" \")[tl-1]))\n",
    "    #type is string\n",
    "    elif isinstance(df[\"DOB\"][i], str):\n",
    "        tl = len(df[\"DOB\"][i].split(\"-\"))\n",
    "        df[\"Age\"][i] += int(2019 - int(df[\"DOB\"][i].split(\"-\")[0])) \n",
    "    #time is float: means it's nan value so do nothing\n",
    "#same index as above\n",
    "ngenage = [0,0,0]\n",
    "j       = 1\n",
    "ss      = 0\n",
    "stdv    = []\n",
    "for each in df['gender']:\n",
    "    if each[0] == \"F\" and df['Age'][j] != 0:\n",
    "        ss   += df['Age'][j]\n",
    "        stdv.append((df['Age'][j]))\n",
    "        ngenage[0] += 1\n",
    "    elif each[0] == \"M\" and df['Age'][j] != 0:\n",
    "        ss += df['Age'][j]\n",
    "        stdv.append((df['Age'][j]))\n",
    "        ngenage[1] += 1\n",
    "    elif df['Age'][j] != 0: \n",
    "        ss += df['Age'][j]\n",
    "        stdv.append((df['Age'][j]))\n",
    "        ngenage[2] += 1\n",
    "    j += 1\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47323\n",
      "[513, 469, 0]\n",
      "48.0\n",
      "17.0\n"
     ]
    }
   ],
   "source": [
    "print(ss)\n",
    "print(ngenage) # as we can see, unknonwn gender will unlikely to have age, don't care about gender U\n",
    "#average age not counting 0 is \n",
    "mean_val = round(ss/sum(ngenage),0)\n",
    "print(mean_val)\n",
    "stdv_val = round(ma.sqrt(1/(sum(ngenage)-1)*sum((stdv - (ss/sum(ngenage)))**2)),0)\n",
    "print(stdv_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pruchases from age 42 - 34/2, 42, 42 + 34/2 \n",
    "age_dict = {}\n",
    "f1  = []\n",
    "bf1 = []\n",
    "f2  = []\n",
    "bf2 = []\n",
    "f3  = []\n",
    "bf3 = []\n",
    "f4  = []\n",
    "bf4 = []\n",
    "m1  = []\n",
    "bm1 = []\n",
    "m2  = []\n",
    "bm2 = []\n",
    "m3  = []\n",
    "bm3 = []\n",
    "m4  = []\n",
    "bm4 = []\n",
    "fq = 42-34/2\n",
    "sq = mean_val\n",
    "tq = 42+34/2\n",
    "jjj = 1\n",
    "for each in df['gender']:\n",
    "    temp = int(df['Age'][jjj])\n",
    "    bkt  = int(df['3y_bike_purchases'][jjj])\n",
    "    if each[0] == \"F\" and temp != 0:\n",
    "        if(temp <= fq):\n",
    "            f1.append(temp)\n",
    "            bf1.append(bkt)\n",
    "        elif(fq < temp and temp <= sq):\n",
    "            f2.append(temp)\n",
    "            bf2.append(bkt)\n",
    "        elif(sq < temp and temp <= tq):\n",
    "            f3.append(temp)\n",
    "            bf3.append(bkt)\n",
    "        elif(tq < temp):\n",
    "            f4.append(temp)\n",
    "            bf4.append(bkt)\n",
    "    elif each[0] == \"M\" and temp != 0:\n",
    "        if(temp <= fq):\n",
    "            m1.append(temp)\n",
    "            bm1.append(bkt)\n",
    "        elif(fq < temp and temp <= sq):\n",
    "            m2.append(temp)\n",
    "            bm2.append(bkt)\n",
    "        elif(sq < temp and temp <= tq):\n",
    "            m3.append(temp)\n",
    "            bm3.append(bkt)\n",
    "        elif(tq < temp):\n",
    "            m4.append(temp)\n",
    "            bm4.append(bkt)\n",
    "    jjj += 1\n",
    "dtt = {\"Female1\":f1,\n",
    "       \"Female2\":f2,\n",
    "       \"Female3\":f3,\n",
    "       \"Female4\":f4,\n",
    "       \"Male1\":m1,\n",
    "       \"Male2\":m2,\n",
    "       \"Male3\":m3,\n",
    "       \"Male4\":m4,\n",
    "      }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25.0 48.0 59.0\n"
     ]
    }
   ],
   "source": [
    "print(fq, sq, tq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61 205 100 147\n",
      "73 172 88 136\n"
     ]
    }
   ],
   "source": [
    "print(len(dtt[\"Female1\"]), len(dtt[\"Female2\"]), len(dtt[\"Female3\"]), len(dtt[\"Female4\"]))\n",
    "print(len(dtt[\"Male1\"]), len(dtt[\"Male2\"]), len(dtt[\"Male3\"]), len(dtt[\"Male4\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1f24ff1a4e0>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fc5e550>,\n",
       "  <matplotlib.axis.XTick at 0x1f24ff3f7b8>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fe00198>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fe007f0>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fe00e80>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fdea518>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fdeaba8>],\n",
       " <a list of 8 Text xticklabel objects>)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f250533dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphgen = [len(dtt[\"Female1\"]), len(dtt[\"Male1\"]), len(dtt[\"Female2\"]), len(dtt[\"Male2\"]), len(dtt[\"Female3\"]), len(dtt[\"Male3\"]), len(dtt[\"Female4\"]), len(dtt[\"Male4\"])]\n",
    "colorr = (0.7,0.7,0.7,0.7)\n",
    "\n",
    "x2 = np.arange(8)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('Number of people')\n",
    "plt.bar(x2, graphgen, color = colorr)\n",
    "for i in range(len(graphgen)):\n",
    "    plt.text(x = i-0.2, y = graphgen[i]+0.1, s = graphgen[i], size = 10)\n",
    "\n",
    "plt.xticks(x2, ('Fq1', \"Mq1\", 'Fq2', \"Mq2\", 'Fq3', \"Mq3\", 'Fq4', \"Mq4\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We should focus on people around second quadrant (25-48) and fourth quardrant > 59"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Financial Services    203\n",
       "Manufacturing         199\n",
       "Health                152\n",
       "Retail                 78\n",
       "Property               64\n",
       "IT                     51\n",
       "Entertainment          37\n",
       "Argiculture            26\n",
       "Telecommunications     25\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check_unique()[\"Category\"]\n",
    "df[\"Category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1f24fe20be0>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fec3c18>,\n",
       "  <matplotlib.axis.XTick at 0x1f24ff24d30>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fd83860>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fd83eb8>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fd7f588>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fd7fc18>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fd612e8>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fd61978>],\n",
       " <a list of 9 Text xticklabel objects>)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pFxFxVR3GZGZm9UAu3VyvknQCyf2SAGZFxCP5DcvMzAotl2dS/wYYDrySvoanZWZm1oDlciV1P6A4IjZBxc36XmTz50mbmVkDk+t1EHtkDH8pH4GYmVn9kssZxG+AFyXNJOnq2hufPZiZNXi5NFKPkzSL5C6uAi6LiHfyHZiZmRVWTlVMEVEWEZMjYtLWJAdJd0paIWlhRtmekqZJ+kf698tpuSTdLGmxpPmSum/92zEzs9qS73sxjQGOqVQ2ApgeER2B6ek4wLEkz6LuCAwDbstzbGZmVo28JoiIeBKo/Gzp/sDYdHgscGJG+V2ReB7YQ1JRPuMzM7OqVZsgJDXJrB6qJW0iogySqiugdVreFliaMd+ytMzMzAqg2gSRXvvwkqT21c1XS5SlLLaYSRomaY6kOStXrqyDsMzMGqdcurkWAS+nz6T+qLxwO55FvVxSUUSUpVVIK9LyZcA+GfO1A/5deeGIGAWMAigpKdkigZiZWe3IJUFcWcvbnAwMAa5J/07KKP9hemvxXsAH5VVRZmZW93J6JrWkfYGOEfG4pC8ATXNZuaRxQB+gpaRlwBUkieEvks4G/gWcnM4+BTgOWAx8DJy1le/FzMxqUY0JQtI5JN1O9wT2J2k4/hNwVE3LRsTgKiZtsWxEBHBBTes0M7O6kUs31wuAw4APASLiH3ze88jMzBqoXBLEZxGxrnxEUjOy9C4yM7OGJZcE8YSknwG7SuoLPAA8nN+wzMys0HJJECOAlcAC4FySxuSf5zMoMzMrvFx6MW1KHxL0d5KqpUVpg7KZmTVgufRi6kfSa+kNkqudvyrp3Ij4a76DMzOzwsnlQrnrgSMjYjGApP2BRwEnCDOzBiyXNogV5ckh9Saf3x7DzMwaqCrPICQNTAdfljQF+AtJG8TJwAt1EJuZmRVQdWcQ301fuwDLgSNIbpuxEvhy3iMzy7Bo0SKKi4srXi1atODGG2+ktLSUQw89lOLiYkpKSpg9e3ahQzVrMKo8g4gI3wvJ6o0DDjiA0tJSADZu3Ejbtm0ZMGAA55xzDldccQXHHnssU6ZM4dJLL2XWrFmFDdasgcilF9NXgR8BHTLn347bfZttl+nTp7P//vuz7777IokPP/wQgA8++IC99967wNGZNRy59GKaCNxBcvX0pvyGY1az++67j8GDk/tA3njjjRx99NFccsklbNq0iWeffbbA0Zk1HLn0Yvo0Im6OiJkR8UT5K++RmWWxbt06Jk+ezMknJ3eJv+2227jhhhtYunQpN9xwA2effXaBIzRrOHJJEDdJukLS1yV1L3/lPTKzLP7617/SvXt32rRpA8DYsWMZODDpcHfyySe7kdqsFuVSxXQQcDrwLT6vYop03KxOjRs3rqJ6CWDvvffmiSeeoE+fPsyYMYOOHTsWMDqzhiWXBDEA2C/zlt9mhfDxxx8zbdo0br/99oqy0aNHM3z4cDZs2MAuu+zCqFGjChihWcOSS4J4CdiDWrp6WtIBwP0ZRfsBl6fbOIfkOguAn0XElNrYpjUMd0+czq9vGcN9jz65Wfmwi0dWDM9Z9G/mLPr3Fsue+1/fzXd4Zg1OLgmiDfCapBeAz8oLt7Wba0QsAooBJDUF3gYmkDyD+oaI+N22rNfMzGpXLgniijxu/yjgjYj4p6Q8bsbMzLZWLs+DyGeX1u8B4zLGfyjpDGAOcHFErK68gKRhwDCA9u3b5zE0M7PGrcZurpLWSPowfX0qaaOkD7d3w5J2Bk4geYQpwG3A/iTVT2UktxnfQkSMioiSiChp1arV9oZhZmZVyOUMYvfMcUknAj1rYdvHAvMiYnm6neUZ2xgNPFIL2zAzs22Uy4Vym4mIidTONRCDyaheklSUMW0AsLAWtmFmZtsolyqmgRmvQZKuIblQbptJ+gLQF3goo/haSQskzQeOBC7cnm2Y5dv777/PoEGD6NSpE507d+a5556rmPa73/0OSaxataqAEZptn1x6MWV2IN8ALAH6b89GI+JjYK9KZadvzzrN6trw4cM55phjePDBB1m3bh0ff/wxAEuXLmXatGnuRGE7vFzaIPxcCLNKPvzwQ5588knGjBkDwM4778zOO+8MwIUXXsi1115L//7b9TvKrOCqe+To5dUsFxFxVR7iMdshvPnmm7Rq1YqzzjqLl156iR49enDTTTcxffp02rZtS7du3Qodotl2q+4M4qMsZV8EziapHnKCsEZrw4YNzJs3j1tuuYVevXoxfPhwRo4cyZNPPsnUqVMLHZ5ZraiykToiri9/AaOAXUluh3Efyf2TzBqtdu3a0a5dO3r16gXAoEGDmDdvHm+99RbdunWjQ4cOLFu2jO7du/POO+8UOFqzbVNtLyZJe0q6GphPcrbRPSIui4hauXGf2Y7qK1/5Cvvssw+LFi0Cksegdu/enRUrVrBkyRKWLFlCu3btmDdvHl/5ylcKHK3ZtqmuDeI6YCDJ2cNBEbG2zqIy2wHccsstnHbaaaxbt4799tuPP//5z4UOyaxWVdcGcTHJ3Vt/Dvxvxs30RNJI3SLPsZnVa39/ZSnnXPT5vSz/8tenN5v+01/fwvipz1VeDPDtx23HUGWCiIitvsrazMwaDicBMzPLygnCzMyycoIwM7OsnCDMGpAOHTpw0EEHUVxcTElJCQAPPPAAXbp0oUmTJsyZM6fAEdqOJJeb9ZnZDmTmzJm0bNmyYrxr16489NBDnHvuuQWMynZEThBmDVznzp0LHYLtoFzFZNaASOI73/kOPXr0YNSoUYUOx3ZwPoMwa0CeeeYZ9t57b1asWEHfvn3p1KkTvXv3LnRYtoMq2BmEpCXpE+RKJc1Jy/aUNE3SP9K/Xy5UfGY7or333huA1q1bM2DAAGbPnl3giGxHVugqpiMjojgiStLxEcD0iOgITE/HzSwHH330EWvWrKkYnjp1Kl27di1wVLYjK3SCqKw/MDYdHgucWMBYzHYoy5cv5/DDD6dbt2707NmTfv36ccwxxzBhwgTatWvHc889R79+/Tj66KMLHartIArZBhHAVEkB3B4Ro4A2EVEGEBFlkloXMD6zHcp+++3HDy67erOy2+99GGjGL669LUv553zzQMumkAnisIj4d5oEpkl6LZeFJA0DhgF+KLyZWR4VrIopIv6d/l0BTAB6AsslFQGkf7d4MFFEjIqIkogoadWqVV2GbGbWqBQkQUj6oqTdy4eB7wALgcnAkHS2IcCkQsRnZmaFq2JqA0xIH0LUDLg3Ih6T9ALwF0lnA/8CTi5QfGZmjV5BEkREvAl0y1L+LnBU3UdkZmaV+UpqM8uLTz/9lN69e/PZZ5+xYcMGBg0axJVXXsk3v/nNius1VqxYQc+ePZk4cWKBo7VsnCDMLC+aN2/OjBkz2G233Vi/fj2HH344xx57LE899VTFPCeddBL9+/cvYJRWnfp2oZyZNRCS2G233QBYv34969evJ213BGDNmjXMmDGDE0/09bD1lROEmeXNxo0bKS4upnXr1vTt25devXpVTJswYQJHHXUULVq0KGCEVh0nCDPLm6ZNm1JaWsqyZcuYPXs2CxcurJg2btw4Bg8eXMDorCZOEGaWd3vssQd9+vThscceA+Ddd99l9uzZ9OvXr8CRWXWcIMwsL1auXMn7778PwCeffMLjjz9Op06dgOQ52ccffzy77LJLIUO0GrgXk5nlRVlZGUOGDGHjxo1s2rSJU045heOPPx6A++67jxEjfDf/+s4Jwszy4uCDD+a8n/xys7Lyu8gOHnYx/3xv/RZ3lS3nu8vWD65iMjOzrJwgzMwsKycIMzPLygnCzBqFpUuXcuSRR9K5c2e6dOnCTTfdVDHtlltu4YADDqBLly5ceumlBYyyfnEjtZk1Cs2aNeP666+ne/furFmzhh49etC3b1+WL1/OpEmTmD9/Ps2bN2fFii2eU5YXS5cu5YwzzuCdd96hSZMmDBs2jOHDhzNy5EhGjx5N+QPRfv3rX3PcccfVSUyVOUGYWaNQVFREUVERALvvvjudO3fm7bffZvTo0YwYMYLmzZsD0Lp16zqJp6qEBXDhhRdyySWX1Ekc1XEVk5k1OkuWLOHFF1+kV69evP766zz11FP06tWLI444ghdeeKFOYigqKqJ79+7A5gmrPnGCMLNGZe3atZx00knceOONtGjRgg0bNrB69Wqef/55rrvuOk455RQiok5jykxYALfeeisHH3wwQ4cOZfXq1XUaS6Y6TxCS9pE0U9Krkl6WNDwtHynpbUml6aswlW5m1mCtX7+ek046idNOO42BAwcC0K5dOwYOHIgkevbsSZMmTVi1alWdxVQ5YZ1//vm88cYblJaWUlRUxMUXX1xnsVRWiDOIDcDFEdEZOBS4QNKB6bQbIqI4fU0pQGxm1kBFBGeffTadO3fmoosuqig/8cQTmTFjBgCvv/4669ato2XLlnUSU7aE1aZNG5o2bUqTJk0455xzmD17dp3Ekk2dN1JHRBlQlg6vkfQq0Lau4zCzxuWZZ57h7rvvpu0++zJ+wmQA+p96Op27duNvM+7jjj/vS9NmzRh0xrmMGvfIFsvX9u0/qkpYZWVlFY3pEyZMoGvXrrW63a1R0F5MkjoAhwB/Bw4DfijpDGAOyVnGFpVvkoYBwwDat29fZ7Ga2Y7t8MMP50/3TM46begP6r4ap6qENefZJ1n6z7eQYK9WbTht6A+y3rOqLu5XVbAEIWk3YDzwPxHxoaTbgKuASP9eDwytvFxEjAJGAZSUlNRtS5KZWS2pKmEdVFxSgGiyK0gvJkk7kSSHeyLiIYCIWB4RGyNiEzAa6FmI2MzMLFGIXkwC7gBejYjfZ5QXZcw2AFhYeVkzM6s7hahiOgw4HVggqTQt+xkwWFIxSRXTEuDcAsRmZmapQvRiehpQlknu1mpmVo/4SmozM8vKCcLMzLJygjAzs6ycIMzMLCsnCDMzy8oJwszMsnKCMDOzrJwgzMwsKycIMzPLygnCzMyycoIwM7OsnCDMzCwrJwgzM8vKCcLMzLJygjAzs6ycIMzMLKt6lyAkHSNpkaTFkkYUOh4zs8aqXiUISU2BPwDHAgeSPIb0wMJGZWbWONWrBAH0BBZHxJsRsQ64D+hf4JjMzBql+pYg2gJLM8aXpWVmZlbHFBGFjqGCpJOBoyPi++n46UDPiPhRxjzDgGHp6AHAojyF0xJYlad1bwvHU736Fg/Uv5gcT/XqWzyQv5j2jYhWNc3ULA8b3h7LgH0yxtsB/86cISJGAaPyHYikORFRku/t5MrxVK++xQP1LybHU736Fg8UPqb6VsX0AtBR0lcl7Qx8D5hc4JjMzBqlenUGEREbJP0Q+BvQFLgzIl4ucFhmZo1SvUoQABExBZhS6Diog2qsreR4qlff4oH6F5PjqV59iwcKHFO9aqQ2M7P6o761QZiZWT3RoBKEpI2SSjNeHSSVSLq50LFlkhSS7s4YbyZppaRH6jiOtZXGz5R06zauq095/OnwNzKmjZE0aCvWVf5/XCjpYUl71DD/HpJ+kOO6n03/dpC0sJrtPiDpC7nGXBsq77daWudaSQdlHBPvSXorHX48x3VUPq6qvQXOtr4PScWSjsthvm06piUNSI+9Ttuw7C8lfbua6Xtl7J93JL2dMb5zFcssq+mzXWj1rg1iO30SEcWVypYAcwoQS3U+ArpK2jUiPgH6Am8XOKba1AdYCzy7jctX/B8ljQUuAH5Vzfx7AD8A/ljTiiOiui+uzO3eA5wH/L58oiSRVMtuqvEdbCVJzdj+/ZZVRCwAyt/XGOCRiHhwK1aR7biqTh+28n2k778YKKGGNsiImMO2HdODgadJekeO3IrYmkbE5TXE9C6f7+ORwNqI+N02xFivNKgziGwq/bIdKelOSbMkvSnpxxnzTZQ0V9LL6cV45eVrJf1K0kuSnpfUJi1vI2lCWv5S+S8mSf8taXb6y+H29P5S2fwV6JcODwbGZWyzp6RnJb2Y/j0gLT9T0kOSHpP0D0nXZsaZMTwo/SLYnv3WStJ4SS+kr8Oqiy1juQ4kX6wXpvvgm+mk3un8b27N2QTwHBlX00v6SRrPfElXpsXXAPun27tO0m6SpkuaJ2mBpP4Zy68lN08B/5Geabwq6Y/APGAfSYPT9S5ual7NAAAG7ElEQVSU9NvMdUu6Pt3udEmt0vL90//ZXElPlf+CVXJm9XtJM4H7K++39Jf+Tum8LSQtKR+vD9J4rszYz52y/f+r+SyNlDRK0lTgLuCXwKnpcqdWcxzkdExXinU34DDgbJIEgaQmkv6YHvOPSJpS/tlM39vlkp4GTtZWngVX2vaQjO+EP0ra4nu3qnkk9Uv370vpfkJSS0mT02PgWUld0/Kr0zinpvGfmH4eF0p6VEkS3joR0WBewEagNH1NSMv6kPxiguRXw7NAc5IrFN8Fdkqn7Zn+3RVYCOyVjgfw3XT4WuDn6fD9wP+kw02BLwGdgYcz1vlH4Iwsca4FDgYeBHZJ482MswXQLB3+NjA+HT4TeDPd1i7AP4F9yteZsf5BwJit3F+lwL+AW9Np9wKHp8PtgVdriK3yfr4kYztjgAdIfpAcSHK/reriWpuxXx8AjknHv0PSq0Ppuh4BegMdgIUZyzcDWqTDLYHFfN4ho3zdmy1TaVozYBJwfjrfJuDQdNre6X5qlc43Azgx47NyWjp8eca+nA50TId7ATMy9ssjQNMq9tufM9Y9DLh+G46JtZXGxwCDtuO4KgVOTcuXAD9Kh38A/F8V76Oqz9JIYC6wa8bn+9aM5XL9rGU9piu9h/8G7kiHnwW6kxwnU9LP0leA1eX7Jn1vl27Lfst8/0BXYGLG+xgF/Fc6vIzk7DfrPGlM/yK56hk+/466DfjfjGNiTjp8NfAEyeeyB/Ax0Ded9jBw/NZ+fhpDFVNlj0bEZ8BnklYAbUj+UT+WNCCdZx+gI8mHbR3JQQzJh7lvOvwt4AyAiNgIfKDk1iA9gBckQZJsVmQLIiLmp7+2BrPlKfWXgLGSOpJ86WT+apweER8ASHoF2JfN71+1NTbbX5LOJDnFh+SAPDB9HwAtJO1eQ2zVmRhJ1cwrSs/CqrGrpFKSL+e5wLS0/Dvp68V0fDeS/9O/Ki0v4NeSepN8ubcl+T+/k+N2ITmDuIMkIfwzIp5Py/8TmBURK6GiKqo3yQG+ieSHA8D/Ax5Kf7l+A3ggY182z9jmA+nnJ5v/Ay5N130WcE4N8edLdcfVQ+nfucDAKuap6rMEMDmSatZscv2sVXVMZxoM3JgO35eO70Sy/zcB76RncpnuZ/t9m+QzMyfjO6Hy8VrVPJ8AMyPinwAR8V46/+GktQ8RMTU9a/hiOm1KJNeTLUinlx87C0iOp63S0BJELj7LGN4INJPUh+Sf9PWI+FjSLJJf6ADrI03B5fNXs24BYyPipznGMhn4Hckvor0yyq8i+WAMSJPIrOriT4cz+yvvwvZrQrI/Njt4Jd1STWzVyYxbVc6V+CQiiiV9iSQ5XwDcnC73m4i4vVJMHSotfxrJL/weEbFe0hJy2ydbfBGmB+xHWxF7piDZj+9X8wX7URXlRMQzSqq4jiA5y1hY1bwFVP5/re7YqOqzBNW8f6o/DrLFkDUOSXuR/KDrKilIzkwDmFDNtmuKLVciueD3F1s7j6SBbH5cZ85f1Xj5vthE8uOWjPGt/r5v8G0QOfoSsDpNDp2AQ3NYZjpJFQSSmkpqkZYNktQ6Ld9T0r7VrONO4JeRNCJWjqe80frMHN/Dckmd07rLATXOXbOpwA/LRySVf8HlEtsaYPcqpuUsPVP6MXBJWvf+N2Bo+qscSW3TfV15e18CVqTJ4UiSs6za8nfgiLQeuCnJL9En0mlNSKotIKkieDoiPgTeUnIjSpToVsW6s+23u0jap/5ci+8h3yq/j6o+SzUtty3HQTaDgLsiYt+I6BAR+wBvkdwE76S0LaINyQ+12vY4cIqkllDR26l9jvM8A3yr/DtE0p7p/E+S/AhCSc+qZRFRG8lsC04QicdIziTmk/xqeb6G+QGGA0emp3JzgS4R8Qrwc2Bquq5pQFFVK4iIZRFxU5ZJ1wK/kfQMya+dXIwg+bU9AyjLcZnq/BgoSRvCXiFpeMw1toeBAdq8kXqbRMSLwEvA9yJiKkl99nPpfn8Q2D2SHiTPpI1x1wH3pLHPITmQXtueGCrFUwb8FJiZxjUvIialkz8CukiaS/KL9Zdp+WnA2ZJeAl6m6mecZNtv9wBfJqMTQwHsqs27uV5Tw/yV30dVn6XKZpJURZVKOpVtOw6yGcyWZwvjSaoPl5G0Od5Okvw/2I7tbCH98Xcl8Hj6nTCVpAqsxnkiYjnJj9BJ6WfnnnSRy4FvpPP+kqT6MS98JbVZLZG0NiJ2q+V1DgL6R8TptbleS0jaLSLWptVQs4HDIqKmtqpGozG2QZjtENL2nmOBGi8es232iJKL1XYGrnJy2JzPIMzMLCu3QZiZWVZOEGZmlpUThJmZZeUEYWZmWTlBmJlZVk4QZmaW1f8HixCBFMKV8AcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f24fec0be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "val = [203, 199, 152, 78, 64, 51, 37, 26, 25]\n",
    "colorr = (0.2,0.3,0.4,0.5)\n",
    "\n",
    "x3 = np.arange(9)\n",
    "fig3, ax3 = plt.subplots()\n",
    "ax3.set_ylabel('Number of people')\n",
    "plt.bar(x3, val, color = colorr, width = 0.4)\n",
    "for i in range(len(val)):\n",
    "    plt.text(x = i-0.25, y = val[i]+0.1, s = val[i], size = 10)\n",
    "\n",
    "plt.xticks(x3, (\"Finance\", \"Manu\", \"Health\", \"Retail\", \"Property\", \"IT\", \"Entertain\", \"Agri\", \"Telecom\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63 190 102 143\n",
      "30 99 46 74\n",
      "41 88 106 0\n"
     ]
    }
   ],
   "source": [
    "#split into 3: M (Mass), H(High), A(Affluent)\n",
    "jjj = 1\n",
    "wsm = {\"q1\":[], \"q2\":[], \"q3\":[], \"q4\":[]}\n",
    "wsh = {\"q1\":[], \"q2\":[], \"q3\":[], \"q4\":[]}\n",
    "wsa = {\"q1\":[], \"q2\":[], \"q3\":[], \"q4\":[]}\n",
    "\n",
    "for each in df['wealth_segement']:\n",
    "    temp = int(df['Age'][jjj])\n",
    "    if each[0] == \"M\" and temp != 0:\n",
    "        if(temp <= fq):\n",
    "            wsm[\"q1\"].append(temp)\n",
    "        elif(fq < temp and temp <= sq):\n",
    "            wsm[\"q2\"].append(temp)\n",
    "        elif(sq < temp and temp <= tq):\n",
    "            wsm[\"q3\"].append(temp)\n",
    "        elif(tq < temp):\n",
    "            wsm[\"q4\"].append(temp)\n",
    "    elif each[0] == \"H\" and temp != 0:\n",
    "        if(temp <= fq):\n",
    "            wsh[\"q1\"].append(temp)\n",
    "        elif(fq < temp and temp <= sq):\n",
    "            wsh[\"q2\"].append(temp)\n",
    "        elif(sq < temp and temp <= tq):\n",
    "            wsh[\"q3\"].append(temp)\n",
    "        elif(tq < temp):\n",
    "            wsh[\"q4\"].append(temp)\n",
    "    elif each[0] == \"A\" and temp != 0:\n",
    "        if(temp <= fq):\n",
    "            wsa[\"q1\"].append(temp)\n",
    "        elif(fq < temp and temp <= sq):\n",
    "            wsa[\"q2\"].append(temp)\n",
    "        elif(sq < temp and temp <= tq):\n",
    "            wsa[\"q3\"].append(temp)\n",
    "        elif(tq < temp):\n",
    "            wsa[\"q3\"].append(temp)\n",
    "    jjj += 1\n",
    "print(len(wsm[\"q1\"]), len(wsm[\"q2\"]), len(wsm[\"q3\"]), len(wsm[\"q4\"]))\n",
    "print(len(wsh[\"q1\"]), len(wsh[\"q2\"]), len(wsh[\"q3\"]), len(wsh[\"q4\"]))\n",
    "print(len(wsa[\"q1\"]), len(wsa[\"q2\"]), len(wsa[\"q3\"]), len(wsa[\"q4\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f24f7669e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 4\n",
    "wsmtp = [len(wsm[\"q1\"]), len(wsm[\"q2\"]), len(wsm[\"q3\"]), len(wsm[\"q4\"])]\n",
    "wshtp = [len(wsh[\"q1\"]), len(wsh[\"q2\"]), len(wsh[\"q3\"]), len(wsh[\"q4\"])]\n",
    "wsatp = [len(wsa[\"q1\"]), len(wsa[\"q2\"]), len(wsa[\"q3\"]), len(wsa[\"q4\"])]\n",
    "\n",
    "bars = np.add(wsmtp, wshtp).tolist()\n",
    "r = [0,1,2,3,4]\n",
    "\n",
    "ind   = np.arange(N) # the x locations for the groups\n",
    "width = 0.35       # the width of the bars: can also be len(x) sequence\n",
    "\n",
    "p1 = plt.bar(ind, wsmtp, width)\n",
    "p2 = plt.bar(ind, wshtp, width,  bottom=wsmtp)\n",
    "p3 = plt.bar(ind, wsatp, width,  bottom=bars)\n",
    "\n",
    "plt.ylabel('Amount of people')\n",
    "plt.title('Wealth segements by age')\n",
    "plt.xticks(ind, ('q1', 'q2', 'q3', 'q4'))\n",
    "plt.yticks(np.arange(0, 500, 100))\n",
    "plt.legend((p1[0], p2[0], p3[0]), ('Mass', 'High Net', 'Affluent'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"state\"].value_counts()\n",
    "nsw_car = 0\n",
    "nsw_nc  = 0\n",
    "vic_car = 0\n",
    "vic_nc  = 0\n",
    "qld_car = 0\n",
    "qld_nc  = 0\n",
    "idx     = 1\n",
    "for es in df[\"state\"]:\n",
    "    if es == \"NSW\":\n",
    "        if df[\"owns_car\"][idx] == \"Yes\":\n",
    "            nsw_car += 1\n",
    "        else:\n",
    "            nsw_nc  += 1\n",
    "    elif es == \"VIC\":\n",
    "        if df[\"owns_car\"][idx] == \"Yes\":\n",
    "            vic_car += 1\n",
    "        else:\n",
    "            vic_nc  += 1\n",
    "    elif es == \"QLD\":\n",
    "        if df[\"owns_car\"][idx] == \"Yes\":\n",
    "            qld_car += 1\n",
    "        else:\n",
    "            qld_nc  += 1\n",
    "    idx += 1\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1f24fcd6b38>,\n",
       "  <matplotlib.axis.XTick at 0x1f2500e7e10>,\n",
       "  <matplotlib.axis.XTick at 0x1f2500d4c50>,\n",
       "  <matplotlib.axis.XTick at 0x1f24f764668>,\n",
       "  <matplotlib.axis.XTick at 0x1f24f764eb8>,\n",
       "  <matplotlib.axis.XTick at 0x1f24fd4ec18>],\n",
       " <a list of 6 Text xticklabel objects>)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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evHnMnTuXgQMHcvXVV1d/tmHDBkaMGMGAAb4v0yyf2noQ75NMRf0cmJ3zMw3wvygrOf369WOPPfbYqKxNmzbV79esWYP01YS5W2+9lcGDB9OuXbtGi3FLbEmPaOLEiZSVlVFWVkbfvn155ZVXihW2NSE1JoiIeCUi7gW+CdzPVwnit+m9C2bbhJEjR9KpUycmTpxY3YNYtmwZU6ZM4fzzzy9ydDXbkh5Rly5dmDFjBvPmzWPUqFEMHz68GCFbE5NlDKIvsAC4Dfg18LakfgWNyqwBXXfddSxZsoShQ4cybtw4AC6++GLGjBlDy5YtixxdzbakR9S3b1923313APr06cPSpUsbL1BrsrKs5noTcGxEvAUgaT+SHkXvQgZm1tDOOOMMjj/+eK666ioqKys5/fTTAVi9ejWPP/44rVq14qSTTipylHUbOXIkEyZMYNddd2X69OmbfT5+/HiOO+64IkRmTU2WHsR2VckBICLeBrYrXEhmDWfBggXV76dNm0b37t0BWLRoEYsXL2bx4sWccsop/PrXv94mkgPk7xFVmT59OuPHj2fMmDFFis6akiwJolLSeElHpj93kYxFmJWUIUOGcPjhh/PWW2/RsWNHxo8fz+WXX86BBx5IWVkZTz75JLfcckuxw2wwZ5xxBpMnT67enjdvHueddx5Tp05lzz33LGJkNcs38H7ZZZfRvXt3ysrKGDRoEB999BEAixcvZqeddqK8vJzy8vKSHi9qqrJcYvoH4ALgpyRrJs0kGYswK6hhY5/aovo7VJzDgIpzqref/wha9z2fir5f1Rk56XWS+zFz9BrKo3+CR7fwfFXuubR/vfarjwULFtCtWzdg4x7Re++9x8knn8x9993Hfvvt12jxbKlhw4Zx4YUXctZZZ1WX9e/fn9GjR9OqVStGjBjB6NGjq3tAXbt2Ze7cucUKt9nL8jyItSTjEH6CnNlW2pKkN+M317PinXl8vuZjdt6tLeXHnsnSN2fxycolqEULdt6tHX1OuYhhY5/i9w/exJL3VzDw1DMBaNGiJQMvvq3ecRYq6fXr14/FixdvVHbsscdWv+/Tp49vWiwhWZbaOAK4Etg3t35EfKNwYZnZd/7+55uVdTss/+Bz3x9cQt8fXFLokAru7rvv5rTTTqveXrRoEQcffDBt2rTh2muv5dvf/nYRo2t+slxiGg/8jGTcwesjmVlBXHfddbRq1YqhQ4cC0KFDB9577z323HNPZs+ezUknncT8+fM3muprhZUlQXzstZHMrJDuvfdeHn30UZ555pnqezt22GEHdthhBwB69+5N165defvtt6moqChmqM1KlgQxXdIvgYeBtVWFETGnYFGZWbPxxBNPMGbMGGbMmMHXvva16vJVq1axxx570LJlS959910WLFjAN77hK9uNKUuCOCx9zU3bARzd8OGYWVM2ZMgQnnvuOVavXk3Hjh256qqrGD16NGvXrqV//2RgvE+fPtxxxx3MnDmTX/ziF7Rq1YqWLVtyxx13bHZnuRVWlllMRzVGIGa27WmIqch/+w+3b1Tn8+rjtuHQc26uLp/8NkzeBqYiNyWN+kQ5MzPbdjhBmJlZXrUmCEktJPWtrY6ZmTVNtSaIiPgSGNtIsZiZWQnJconpSUmDlfsoLjMza/KyTHO9BNgZ2CDpryQL9kVE+HZGM7MmLMs019aNEYiZmZWWLD0IJJ0AVD1m9LmIeLRwIZmZWSmocwxC0g3ARSSL6L8OXJSWmZk1a/kegPTnP/+Z/v37061bN/r378+HH34IwNSpUykrK6O8vJyKigpeeOGFYoWdWZZB6u8D/SPi7oi4G/heWmZm1qwNGzaMJ554YqOyG264gWOOOYYFCxZwzDHHcMMNyffpY445hldeeYW5c+dy9913c9555xUj5C2S9Ua53XLe71qIQMzMtjX9+vXbbH2oqVOncvbZZwNw9tln88gjjwCwyy67VK9Uu2bNGraFiaFZxiBGAy9Lmk4yg6kfcEVBozIz20atWLGCDh06AMkzLVauXFn92ZQpU7jiiitYuXIljz32WLFCzKzOHkRE3A/0IVnu+2Hg8IiYVOjAzMyamkGDBvHmm2/yyCOPMGrUqGKHU6dMl5giYnlETIuIqRHx/wsdlJnZtqp9+/YsX74cgOXLl9OuXbvN6vTr14933nmH1atXN3Z4W6Rgi/VJulvSSkmv5ZTtIekpSQvS193Tckn6laSFkuZJOqRQcZmZFdIJJ5zAvffeCyRPyjvxxBMBWLhwIREBwJw5c/jiiy/Yc889ixZnFpnug6ine4BxwIScssuBZyLiBkmXp9sjgOOAbunPYcDtfPWgIjOzRrMlz7iY8ZvrWfHOPD5f8zE779aW8mPPpNOBR/Cb8ddy/dhb2Xm3dhx51j8zbOxTvPrsA7wz+2latGxJq+12oPegf+SHNz1d7zgb4xkXdSYISV2BpRGxVtKRQBkwISI+qm2/iJgpqfMmxScCR6bv7wWeI0kQJ6bHDOAlSbtJ6hARy7M3xcyscX3n73+et3zA+f+6WVmvo0+j19GnFTqkBpXlEtNkknWYvgmMB7oA/1XP87Wv+qOfvlZdnNsHWJJTb2laZmZmRZIlQXwZEeuBQcDNEfEzoEMDx5FvQnDkrSgNl1QpqXLVqlUNHIaZmVXJkiDWSRoCnA1UrcG0XT3Pt0JSB4D0tWqC8FKgU069jsD7+Q4QEXdGREVEVLRt27aeYZiZWV2yJIgfAocD10XEIkldgN/U83zTSBIN6evUnPKz0tlMfYCPPf5gZlZcWZb7fh34ac72IqDOxfok3U8yIL2XpKXAv6T7PSjpXOA94NS0+uMk6zstBD4jSUpmZlZEWWYxHQFcCeyb1q96YNA3atsvIobU8NExeeoGcEFdsZiZWePJch/EeOBnwGxgQ2HDMTOzUpElQXwcEb8reCRmZlZSsiSI6ZJ+SbJQ39qqwoiYU7CozMys6LIkiKolLypyygI4uuHDMTOzUpFlFtNRjRGImZmVliyzmN4BXgKeB2am017NzKyJy3Kj3AHAvwN7AjdKelfSlMKGZWZmxZYlQWwA1qWvXwIr+GqJDDMza6KyDFJ/ArwK3ATcFREfFDYkMzMrBVl6EEOAmcCPgUmSrpK02d3QZmbWtGSZxTQVmCqpO8mT3y4G/gnYqcCxmZlZEdXZg5A0OZ3JdAuwC3AWsHuhAzMzs+LKMgZxAzAnIrwOk5lZM5IlQcwFLpDUL92eAdwREesKF5aZmRVblgRxO8kT5H6dbp+Zlp1XqKDMzKz4siSIQyPioJztZyW9UqiAzMysNGS6UU5S16oNSd/Az4UwM2vysvQgLiNZ8vtdkqfJ7YsfCWpm1uRluQ/iGUndgP1JEsSbEbG2jt3MzGwbl6UHQZoQ5hU4FjMzKyFZxiDMzKwZqjVBKNGpsYIxM7PSUWuCiIgAHmmkWMzMrIRkucT0kqRDCx6JmZmVlCyD1EcBP5L0J2ANyUymiIiygkZmZmZFlSVBHFfwKMzMrORkuQ/iTwCS2gE7FjwiMzMrCVmeB3GCpAXAIpKVXBcDvytwXGZmVmRZBqmvAfoAb0dEF+AY4MWCRmVmZkWXJUGsi4gPgBaSWkTEdKB8a04qabGkVyXNlVSZlu0h6SlJC9JXP7XOzKyIsiSIjyTtAswEJkq6BVjfAOc+KiLKI6Ii3b4ceCYiugHPpNtmZlYkWRLEicBnwM+AJ4B3gL8rQCwnAvem7+8FTirAOczMLKMaE4Skb0o6IiLWRMSXEbE+Iu4leQTpblt53gCelDRb0vC0rH1ELAdIX9tt5TnMzGwr1NaDuBn4S57yz9LPtsYREXEIyT0Wuc+7rpOk4ZIqJVWuWrVqK8MwM7Oa1JYgOkfEZkt8R0Ql0HlrThoR76evK4EpwLeAFZI6AKSvK2vY986IqIiIirZt225NGGZmVovaEkRtN8XtVN8TStpZUuuq98CxwGvANODstNrZwNT6nsPMzLZebXdSz5L0/yLirtxCSecCs7finO2BKZKqzv9fEfGEpFnAg+nx3wNO3YpzmJnZVqotQVxM8od8KF8lhApge2BQfU8YEe8CB+Up/4DkJjwzMysBNSaIiFgB9JV0FHBgWvxYRDzbKJGZmVlRZVmsbzowvRFiMTOzEuJnUpuZWV5OEGZmlpcThJmZ5eUEYWZmeTlBmJlZXk4QZmaWlxOEmZnl5QRhZmZ5OUGYmVleThBmZpaXE4SZmeXlBGFmZnk5QZiZWV5OEGZmlpcThJmZ5eUEYWZmeTlBmJlZXk4QZmaWlxOEmZnl5QRhZmZ5OUGYmVleThBmZpaXE4SZmeXlBGFmZnk5QZiZWV5OEGZmlpcThJmZ5VVyCULS9yS9JWmhpMuLHY+ZWXNVUglCUkvgNuA44ABgiKQDihuVmVnzVFIJAvgWsDAi3o2IL4BJwIlFjsnMrFkqtQSxD7AkZ3tpWmZmZo1MEVHsGKpJOhUYEBHnpdtnAt+KiJ/k1BkODE839wfeavRAa7YXsLrYQTSwptamptYeaHptamrtgdJr074R0bauSq0aI5ItsBTolLPdEXg/t0JE3Anc2ZhBZSWpMiIqih1HQ2pqbWpq7YGm16am1h7YdttUapeYZgHdJHWRtD1wOjCtyDGZmTVLJdWDiIj1ki4E/gdoCdwdEfOLHJaZWbNUUgkCICIeBx4vdhz1VJKXvrZSU2tTU2sPNL02NbX2wDbappIapDYzs9JRamMQZmZWIpwgSpikkrsEaGbNR7NPEJI6S3pD0l2S5kt6UtJOkn4q6XVJ8yRNSuu+Kmk3JT6QdFZafp+k79Zw/JaSbkz3nSfpJ2n5LyTNkvSapDslKS1/TtL1kmYAFzXSf4ZtlqS9JT1U7DgKoSm1TdI9kk7JU36kpEeLEVNDaKrtqtLsE0SqG3BbRPQEPgIGA5cDB0dEGXB+Wu9F4AigJ/Au8O20vA/wUg3HHg50yTnWxLR8XEQcGhEHAjsBA3P22S0ivhMRY+vboEZIfMMkPSzpCUkLJP1rzmffkzRH0iuSnqlvG7KIiPcjYrN/oIWSrhfWKBq7bU1NY/6/aqqcIBKLImJu+n420BmYB0yU9PfA+vSz54F+6c/tQC9J+wB/johPazj2d4E7ImI9QET8OS0/StIfJL0KHE2SdKo80DDNKmjiAygHTgN6AadJ6iSpLXAXMDgiDgJObaC2IGmMpB/nbF8p6VJJr6XbeXtrNRzrUEm/T5PYHyW1TpPq82lymyOpb1r3SEnTJf0X8GpDtaeAbVss6aq0Da9K6p6W7yLpP3OOMbhAbRmpZEXmpyXdL+kfN/n8e5LelPQCcHIdx8obs6TbJVWmX36uyqm/OO2dv0AD/u4VoF1XSro7vWLwrqSf5nx2VtrWVyTd15Bt2FJOEIm1Oe83kEz/PZ5kZdnewGwl4wEzSf54fht4DlgFnEKSOGoiYKOpYpJ2BH4NnBIRvUj+oO6YU2XNVrQlVyETH8AzEfFxRHwOvA7sS5JUZkbEItgoITaESSQJqcoPSG6urFJTb20jSm7CfAC4KE1i3wX+CqwE+kfEIel5fpWz27eAkRFRqNWFG6RtOVan7bgdqPpDNgr4OCJ6pcd4tkEizyGpN8kNrgeT/JE8dJPPdyT5ff87kn9Hf1PHIWuKeWR6Z3IZ8B1JZTn7fB4RfxsRk7a6QV/F3dDtAugODCD53foXSdtJ6gmMBI5OfzeLepnZCSK/FkCniJgO/BOwG7BLRCwhWVOlW0S8C7xA8o+vtgTxJHB+mmCQtAdfJYPVknYhSTKFUMjEV9PxN0uIDSUiXgbaKbk2fxDwIfBeTpWaemub2h9YHhGz0nqfpPtsB9yV9ur+m2TJ+Sp/rEp6hdCAbavycPpa9cWg6hi35ZzzwwYIfVPfBqZExGcR8Qmbr4TQneSLy4JI5tj/po7j1RTzDyTNAV4m6fnm/r9qqB54roZuF8BjEbE2IlaTfDlpT3I14aG0rKG/YG0xz5LJryXwG0m7kvzB+7eI+Cj97A/p55D8AR1Nkihq8h/AfsA8SeuAuyJinKS7SC5XLGbjb4qFVJ340m7wGaSJT9JewPYR8W762T8CF9bjHP8L3CapS0QskrRHA/+SP0SSvP6G5Ft3rqzJqaZ6PwOLObb2AAACRElEQVRWAAeR/Lf6POezhurV1aYh2lalKnlXJe76HKO+6jrHlsSQrwfeheT389CI+FDSPRSmB76phmwXNPIXrPpo9j2IiFicDhRXbd8YESPTLmqviDgwIm7I+fzMiDgjff/7iGgRER/Ucvz1EXFJRBwQEQdFxLi0/J8j4psR8d2I+GFEXJmWHxkRlQVqblXie5Xkm9emie/t9P3zJMus15b48oqIVSSXQx6W9AoN/21uEklX/xSSP6i58vXW8nkT2FvSoWm91uk+u5L0LL4EzuSrLwKNpSHaVpsnyUn6knavZ5y1mQkMUjIhojXJJZdcbwJdJHVNt4fUcbx8MbchSQIfS2pP8oCxQmvodtXkGZLe0Z5Q7//PDcY9iCYqIhYDGyW+OuqfmfP+99Tx5SEi7gHuydkemPP+d8DvtjDkTCJifvoPdFlELJfUOefjzXprwLg8x/hC0mnArZJ2Ihl/+C7JuNBkJcvOT6dxeg25cW112+pwLUnv7jWSb6xX8dWlqAYREXMkPQDMBf7EJpcpI+JzJUv2PyZpNcmXkAM3P1LNMUfEw5JeBuaTTKp4sSHbkE8B2lXTeeZLug6YIWkDyRe5YVsbf315qY0GImkAMGaT4kURMagY8ZiVAklXAp/W9QVlW9NU27Up9yAaSET8D8kqtE2KE59Z8+UehDVpkqaQTA/NNSJN6Nu0ptQ2ST9k8ymdL0bEBcWIp6Fs6+1ygjAzs7ya/SwmMzPLzwnCzMzycoIwM7O8nCDMzCwvJwgzM8vr/wAju6LcTVpggQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f250279dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphgen = [nsw_car, nsw_nc, vic_car, vic_nc, qld_car, qld_nc]\n",
    "colorr = (0.3,0.5,0.7,0.9)\n",
    "\n",
    "x2 = np.arange(6)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('Car owns or not in each states')\n",
    "plt.bar(x2, graphgen, color = colorr)\n",
    "for i in range(len(graphgen)):\n",
    "    plt.text(x = i-0.2, y = graphgen[i]+0.1, s = graphgen[i], size = 10)\n",
    "\n",
    "plt.xticks(x2, ('nsw_car', \"nsw_nc\", 'vic_car', \"vic_nc\", 'qld_car', \"qld_nc\"))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}